feat(notebooks): palliative_care_rfi — PRISMA flow + RFI payment context (closes #647, refs #646, P43)
The P42/P43 deliverable for the CY2027 NPRM Community-Based Palliative Care RFI (91 FR 43949-43950): PRISMA 2020 flow diagram and screening crosstabs rendered live from Zotero project tags (prisma.flow + mo.mermaid, skin-subs pattern), stage-2 included-studies table, and payment context for the code families the RFI names — ACP, CCM, PCM, TCM, APCM priced CY2026-final vs CY2027-proposed from pfs.rvu / pfs.rvu_proposed at each vintage's own CF, MSSP attribution overlap flagged, RVU YoY heatmap 2015-2027p. Citations route through bib.frlink/cfrlink (#642 pattern); year displays through conf.display.plain_years (#643); relevance-capped identification and single-reviewer local-LLM screening are disclosed inline as methods limitations.
This commit is contained in:
kert
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notebooks/palliative_care_rfi.py
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682
notebooks/palliative_care_rfi.py
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import marimo
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__generated_with = "0.23.1"
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app = marimo.App(width="medium")
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@app.cell(hide_code=True)
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def _():
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import marimo as mo
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return (mo,)
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@app.cell(hide_code=True)
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def _(fr_md, mo):
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mo.md(f"""
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# Community-Based Palliative Care RFI — Evidence Base & Payment Context
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The CY2027 PFS NPRM (CMS-1848-P, {fr_md("91 FR 43842")}, docket
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CMS-2026-2377) carries a Request for Information on
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**Community-Based Palliative Care** at {fr_md("91 FR 43949")}-43950.
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This notebook is the P42/P43 deliverable behind the comment we will
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file to that docket: a PRISMA 2020 systematic evidence base for the
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RFI's five questions, plus the payment context for the code
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families the RFI itself names — read live from the repo's lake and
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registries, nothing hard-coded.
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The RFI asks five things:
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- **(a)** where CMS should focus **fraud, waste, and abuse**
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scrutiny in community-based palliative care;
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- **(b)** whether eligibility for a serious-illness service should
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be gated on **certified life expectancy**, and what interval the
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evidence supports;
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- **(c)** whether eligibility is better defined by **impact on
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daily function (ADLs)** and/or **caregiver strain**;
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- **(d)** which **care-management service elements**
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(interdisciplinary teams, home visits, timely follow-up) should
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be required — explicitly tied to section II.E of the same rule;
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- **(e)** how **quality** of palliative care management should be
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measured (MIPS Value Pathway reporting, CBE 3665 "feeling heard
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and understood").
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CMS asks that every statement be supported by peer-reviewed or
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institutional evidence — so the evidence base *is* the response.
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""")
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return
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@app.cell(hide_code=True)
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def _():
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import altair as alt
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import polars as pl
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from conf import connect
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from conf.display import plain_years
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from pfs.rules import RULES, proposed_for
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connect.theme()
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# connect.theme() puts assets/ on sys.path — reuse the HTI-5 design
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# tokens: AMBER/TEAL are the gain/loss diverging pair, INDIGO_MED
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# the default series, PLUM/GRAY categorical follow-ons.
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from fhirworx import AMBER, GRAY, INDIGO_MED, PLUM, TEAL
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# PFS reference data lives in the DuckLake lakehouse (M5, #514) —
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# notebooks connect read-only.
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con = connect.ducklake()
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def q(sql):
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return con.execute(sql).pl()
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def cfr_md(ref, text=""):
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"""eCFR jump link as markdown (P41, #641) — degrades to plain
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text on a malformed cite so the notebook never breaks."""
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try:
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from bib import cfrlink
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return cfrlink.md_link(ref, text=text)
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except Exception:
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return text or ref
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def fr_md(ref, text=""):
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"""FR web jump link as markdown (P40, #638) — degrades to plain
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text when bib.sqlite or the anchor map is unavailable so the
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notebook never breaks offline."""
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try:
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from bib import frlink
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return frlink.md_link(ref, store=connect.bib(), text=text or ref)
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except Exception:
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return text or ref
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return (
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AMBER,
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GRAY,
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INDIGO_MED,
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PLUM,
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RULES,
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TEAL,
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alt,
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cfr_md,
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con,
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connect,
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fr_md,
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pl,
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plain_years,
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proposed_for,
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q,
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)
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# ── 1. PRISMA evidence base ─────────────────────────────────────────────
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## 1. PRISMA 2020 Evidence Base — `project:palliative-rfi`
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**Identification** (2026-08-18): five PubMed E-utilities searches,
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one per RFI part, via
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`dev/scripts/search_pubmed_palliative_rfi.py` — the same pipeline
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the skin-substitutes review (P29) uses. The palliative literature
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dwarfs the skin-subs corpus, so each domain search was
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**relevance-sorted and capped**; capped identification is a stated
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limitation of this review, not silent truncation:
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| RFI part | Domain search | Matching | Retrieved (cap) |
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|---|---|---:|---:|
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| (a) | fraud/waste/abuse in hospice & palliative care | 177 | 177 (250) |
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| (b) | eligibility via prognosis / life expectancy | 655 | 250 |
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| (c) | eligibility via function / caregiver strain | 665 | 250 |
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| (d) | community-based delivery & care management | 1,446 | 300 |
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| (e) | quality measures | 1,671 | 250 |
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1,227 retrieved records dedupe to **1,189 unique articles**, stored
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in bib.sqlite and pushed to Zotero tagged `module:palliative-rfi` +
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`rfi:{a-e}`.
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**Screening** (stage 2, title/abstract) runs through the repo's
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PRISMA pipeline (`stack prisma screen palliative-rfi`) against the
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project's criteria/reasons anchors in Zotero, using a **local
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self-hosted model (qwen2.5:14b via Ollama on the server GPU)** —
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decisions, reason codes, and rationales land back in Zotero as
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tags and notes. The flow below is computed **live from those
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tags**, so it always reflects current screening progress.
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> **Methods disclosures:** single-reviewer LLM screening (no human
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> dual-screening yet); stage-3 full-text eligibility and data
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> extraction have not run; identification is relevance-capped as
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> tabulated above; grey literature (OIG/GAO/MedPAC/CMMI) enters
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> via the bib evidence base rather than PubMed and bypasses
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> clinical screening per the project criteria.
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""")
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return
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@app.cell(hide_code=True)
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def _(connect, mo):
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from prisma import flow as _flow
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class _RoDb:
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"""Duck-typed shim: prisma.flow only touches ``.con``."""
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def __init__(self, con):
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self.con = con
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_zcon = connect.zotero()
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flow_counts = _flow.count(_RoDb(_zcon), "palliative-rfi")
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flow_mermaid = _flow.mermaid(flow_counts, project="palliative-rfi")
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_screened_pct = (
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100 * flow_counts.screened / flow_counts.identified
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if flow_counts.identified
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else 0.0
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)
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mo.vstack(
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[
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mo.mermaid(flow_mermaid),
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mo.md(f"""
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**{flow_counts.identified:,} identified** →
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**{flow_counts.screened:,} screened** ({_screened_pct:.0f}%
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of identified so far) → **{flow_counts.included}
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included** at the current pass. Diagram and counts are
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regenerated from Zotero tags on every notebook run —
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re-run after more screening completes and the flow
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updates itself.
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"""),
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]
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)
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return (flow_counts,)
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@app.cell(hide_code=True)
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def _(AMBER, GRAY, TEAL, alt, connect, mo, pl):
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# Screening status per RFI domain, live from Zotero tags.
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_zcon = connect.zotero()
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_rows = _zcon.execute("""
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WITH proj AS (
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SELECT it.itemID
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FROM itemTags it JOIN tags t ON it.tagID = t.tagID
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WHERE t.name = 'project:palliative-rfi'
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AND it.itemID NOT IN (
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SELECT it2.itemID FROM itemTags it2
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JOIN tags t2 ON it2.tagID = t2.tagID
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WHERE t2.name LIKE 'prisma:%')
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),
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dom AS (
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SELECT it.itemID, t.name AS rfi
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FROM itemTags it JOIN tags t ON it.tagID = t.tagID
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WHERE t.name IN ('rfi:a','rfi:b','rfi:c','rfi:d','rfi:e')
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AND it.itemID IN (SELECT itemID FROM proj)
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),
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decision AS (
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SELECT it.itemID, t.name AS decision
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FROM itemTags it JOIN tags t ON it.tagID = t.tagID
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WHERE t.name IN ('screen:include','screen:exclude','screen:uncertain')
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)
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SELECT dom.rfi,
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coalesce(decision.decision, 'unscreened') AS decision,
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count(*) AS n
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FROM dom LEFT JOIN decision ON dom.itemID = decision.itemID
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GROUP BY dom.rfi, decision.decision
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ORDER BY dom.rfi
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""").fetchall()
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_labels = {
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"rfi:a": "(a) fraud/waste/abuse",
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"rfi:b": "(b) prognosis eligibility",
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"rfi:c": "(c) function/caregiver",
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"rfi:d": "(d) delivery & mgmt",
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"rfi:e": "(e) quality measures",
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}
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rfi_status = pl.DataFrame(
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[
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{"rfi_part": _labels[r], "decision": d.removeprefix("screen:"), "n": n}
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for r, d, n in _rows
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]
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)
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_order = ["include", "uncertain", "exclude", "unscreened"]
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rfi_status_chart = (
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alt.Chart(rfi_status.to_pandas())
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.mark_bar()
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.encode(
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x=alt.X("n:Q", title="Records"),
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y=alt.Y("rfi_part:N", title=None),
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color=alt.Color(
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"decision:N",
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title="Stage-2 decision",
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sort=_order,
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scale=alt.Scale(
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domain=_order,
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range=[TEAL, "#C9A227", AMBER, GRAY],
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),
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),
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order=alt.Order("color_decision_sort_index:Q"),
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tooltip=["rfi_part", "decision", "n"],
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)
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.properties(
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title="Screening Status by RFI Domain (live)", width=620, height=220
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)
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)
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mo.vstack(
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[
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rfi_status_chart,
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mo.md("""
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Records retrieved by more than one domain search carry
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multiple `rfi:*` tags and count once per domain here —
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the PRISMA flow above deduplicates; this chart
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deliberately does not, since a paper can inform two RFI
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answers.
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"""),
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]
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)
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return
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@app.cell(hide_code=True)
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def _(connect, mo, pl, plain_years):
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# Included studies, live: one row per include, with domain tags.
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_zcon = connect.zotero()
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_rows = _zcon.execute("""
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WITH inc AS (
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SELECT it.itemID
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FROM itemTags it JOIN tags t ON it.tagID = t.tagID
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WHERE t.name = 'screen:include'
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AND it.itemID IN (
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SELECT it2.itemID FROM itemTags it2
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JOIN tags t2 ON it2.tagID = t2.tagID
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WHERE t2.name = 'project:palliative-rfi')
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)
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SELECT
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(SELECT idv.value FROM itemData id
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JOIN itemDataValues idv ON idv.valueID = id.valueID
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WHERE id.itemID = inc.itemID
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AND id.fieldID = (SELECT fieldID FROM fields
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WHERE fieldName = 'title')) AS title,
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(SELECT idv.value FROM itemData id
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JOIN itemDataValues idv ON idv.valueID = id.valueID
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WHERE id.itemID = inc.itemID
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AND id.fieldID = (SELECT fieldID FROM fields
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WHERE fieldName = 'publicationTitle')) AS journal,
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(SELECT group_concat(t.name, ', ') FROM itemTags it
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JOIN tags t ON it.tagID = t.tagID
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WHERE it.itemID = inc.itemID
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AND t.name LIKE 'rfi:%') AS rfi_parts,
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(SELECT group_concat(t.name, ', ') FROM itemTags it
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JOIN tags t ON it.tagID = t.tagID
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WHERE it.itemID = inc.itemID
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AND t.name LIKE 'year:%') AS year
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FROM inc
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ORDER BY year DESC
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""").fetchall()
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included = pl.DataFrame(
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[
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{
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"year": (y or "").removeprefix("year:"),
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"title": ti or "",
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"journal": j or "",
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"rfi_parts": r or "",
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}
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for ti, j, r, y in _rows
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]
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)
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mo.vstack(
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[
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mo.md(f"""
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### Included at title/abstract stage — {included.height} records (live)
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Stage-2 includes plus their RFI-domain tags. Every include
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still awaits stage-3 full-text eligibility, so this list
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can only shrink; the LLM's per-record rationale sits on
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each Zotero item as a child note for reviewer audit.
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"""),
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mo.ui.table(
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plain_years(included).to_pandas(),
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page_size=25,
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label="Stage-2 included records",
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),
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]
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)
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return
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# ── 2. Payment context ─────────────────────────────────────────────────
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@app.cell(hide_code=True)
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def _(cfr_md, fr_md, mo):
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mo.md(f"""
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## 2. Payment Context — the Code Families the RFI Names
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The RFI's background ({fr_md("91 FR 43949")}) frames the question
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around whether current **E/M, care management, and ACP** billing
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reflects palliative practice, and part (e) names **APCM (HCPCS
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G0556-G0558)** explicitly. This section prices those families —
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CY2026 Final (the current baseline) against the CY2027 NPRM
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proposal — from `pfs.rvu` / `pfs.rvu_proposed`, at each vintage's
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own non-QP standard CF from `pfs.rules`, national unadjusted
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(GPCI = 1.0). Codes on the MSSP primary-care attribution list
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({cfr_md("42 CFR 425.400(c)")}, `cms.primary_care_service_code`)
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are flagged: for those, valuation moves clinician revenue **and**
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ACO attribution denominators at once.
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Families and code sets are declared in this cell's source —
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membership in each PFS table is queried, never assumed, and codes
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a table doesn't price are reported as such.
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""")
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return
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@app.cell(hide_code=True)
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def _(RULES, pl, proposed_for, q):
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_families = {
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"ACP": ["99497", "99498"],
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"CCM": ["99437", "99439", "99487", "99489", "99490", "99491"],
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"PCM": ["99424", "99425", "99426", "99427"],
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"TCM": ["99495", "99496"],
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"APCM": ["G0556", "G0557", "G0558"],
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}
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_fam_rows = [
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{"hcpcs": c, "family": fam} for fam, codes in _families.items() for c in codes
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]
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fam_df = pl.DataFrame(_fam_rows)
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_codes_sql = ",".join(f"'{c}'" for c in fam_df["hcpcs"])
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_raw = q(f"""
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WITH fin AS (
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SELECT hcpcs, description,
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work_rvu + coalesce(non_fac_pe_rvu, fac_pe_rvu) + mp_rvu
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AS total_2026f
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FROM pfs.rvu
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WHERE year = 2026 AND (mod IS NULL OR mod = '')
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AND hcpcs IN ({_codes_sql})
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QUALIFY row_number() OVER (PARTITION BY hcpcs ORDER BY hcpcs) = 1
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),
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prop AS (
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SELECT hcpcs, description AS description_p,
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work_rvu + coalesce(non_fac_pe_rvu, fac_pe_rvu) + mp_rvu
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AS total_2027p
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FROM pfs.rvu_proposed
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WHERE cms_rule_id = 'CMS-1848-P' AND (mod IS NULL OR mod = '')
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AND hcpcs IN ({_codes_sql})
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QUALIFY row_number() OVER (PARTITION BY hcpcs ORDER BY hcpcs) = 1
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),
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pcs AS (
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SELECT hcpcs_code AS hcpcs, 1 AS attribution_code
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FROM cms.primary_care_service_code
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)
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SELECT coalesce(fin.hcpcs, prop.hcpcs) AS hcpcs,
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coalesce(fin.description, prop.description_p) AS description,
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fin.total_2026f, prop.total_2027p,
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coalesce(pcs.attribution_code, 0) = 1 AS mssp_attribution
|
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FROM fin
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FULL JOIN prop ON fin.hcpcs = prop.hcpcs
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LEFT JOIN pcs ON coalesce(fin.hcpcs, prop.hcpcs) = pcs.hcpcs
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""")
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_cf26 = RULES[2026].conversion_factor
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_cf27p = proposed_for(2027).conversion_factor
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family_pay = (
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fam_df.join(_raw, on="hcpcs", how="left")
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.with_columns(
|
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(pl.col("total_2026f") * _cf26).round(2).alias("dollar_2026final"),
|
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(pl.col("total_2027p") * _cf27p).round(2).alias("dollar_2027proposed"),
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)
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.with_columns(
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(pl.col("dollar_2027proposed") - pl.col("dollar_2026final"))
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.round(2)
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.alias("dollar_delta"),
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pl.when(
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pl.col("total_2026f").is_not_null()
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& pl.col("total_2027p").is_not_null()
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||||
)
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||||
.then(
|
||||
(
|
||||
100
|
||||
* (pl.col("dollar_2027proposed") - pl.col("dollar_2026final"))
|
||||
/ pl.col("dollar_2026final")
|
||||
).round(2)
|
||||
)
|
||||
.alias("pct_change"),
|
||||
)
|
||||
.sort("family", "hcpcs")
|
||||
)
|
||||
family_pay
|
||||
return (family_pay,)
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(AMBER, RULES, TEAL, alt, family_pay, fr_md, mo, pl, proposed_for):
|
||||
_priced = family_pay.filter(
|
||||
pl.col("dollar_2026final").is_not_null()
|
||||
& pl.col("dollar_2027proposed").is_not_null()
|
||||
).with_columns(
|
||||
pl.when(pl.col("pct_change") >= 0)
|
||||
.then(pl.lit("Gain"))
|
||||
.otherwise(pl.lit("Loss"))
|
||||
.alias("sign"),
|
||||
(pl.col("hcpcs") + pl.when(pl.col("mssp_attribution")).then(pl.lit(" ●")).otherwise(pl.lit(""))).alias(
|
||||
"code_label"
|
||||
),
|
||||
)
|
||||
_unpriced = family_pay.filter(
|
||||
pl.col("dollar_2026final").is_null() | pl.col("dollar_2027proposed").is_null()
|
||||
)
|
||||
|
||||
family_chart = (
|
||||
alt.Chart(_priced.to_pandas())
|
||||
.mark_bar()
|
||||
.encode(
|
||||
x=alt.X("pct_change:Q", title="% change, CY2026 final → CY2027 proposed (national, GPCI=1.0)"),
|
||||
y=alt.Y("code_label:N", title=None, sort=alt.SortField(field="pct_change", order="descending")),
|
||||
color=alt.Color(
|
||||
"sign:N",
|
||||
title=None,
|
||||
scale=alt.Scale(domain=["Gain", "Loss"], range=[TEAL, AMBER]),
|
||||
legend=alt.Legend(orient="top"),
|
||||
),
|
||||
tooltip=[
|
||||
"hcpcs", "family", "description", "mssp_attribution",
|
||||
alt.Tooltip("dollar_2026final:Q", format="$.2f"),
|
||||
alt.Tooltip("dollar_2027proposed:Q", format="$.2f"),
|
||||
alt.Tooltip("pct_change:Q", format="+.2f"),
|
||||
],
|
||||
)
|
||||
.properties(
|
||||
title="ACP / Care-Management / APCM Codes — Proposed Payment Change",
|
||||
width=620,
|
||||
height=max(220, 16 * _priced.height),
|
||||
)
|
||||
.facet(row=alt.Row("family:N", title=None))
|
||||
.resolve_scale(y="independent")
|
||||
)
|
||||
|
||||
_missing_lines = (
|
||||
"\n".join(
|
||||
f" - `{r['hcpcs']}` ({r['family']}) — priced in "
|
||||
+ (
|
||||
"CY2026 final only"
|
||||
if r["dollar_2026final"] is not None
|
||||
else "CY2027 proposal only"
|
||||
if r["dollar_2027proposed"] is not None
|
||||
else "neither table"
|
||||
)
|
||||
for r in _unpriced.iter_rows(named=True)
|
||||
)
|
||||
or " - (none — every declared code prices in both tables)"
|
||||
)
|
||||
|
||||
mo.vstack(
|
||||
[
|
||||
family_chart,
|
||||
mo.md(f"""
|
||||
● = MSSP designated primary care service (attribution
|
||||
code). One-sided or unpriced codes, excluded from the
|
||||
chart rather than shown with a fabricated baseline:
|
||||
|
||||
{_missing_lines}
|
||||
|
||||
**Sources:** `pfs.rvu` CY2026 Final
|
||||
({fr_md(RULES[2026].federal_register_citation)});
|
||||
`pfs.rvu_proposed` `CMS-1848-P`
|
||||
({fr_md(proposed_for(2027).federal_register_citation)}) —
|
||||
**final rule pending**. Dollars blend RVU and CF changes
|
||||
deliberately: this is what the proposal does to realized
|
||||
national payment for each service.
|
||||
"""),
|
||||
]
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(AMBER, TEAL, alt, family_pay, mo, pl, q):
|
||||
# Long-term RVU trajectory for the declared codes: YoY % heatmap,
|
||||
# final rules 2015-2026 + CY2027 proposal.
|
||||
_codes_sql = ",".join(f"'{c}'" for c in family_pay["hcpcs"])
|
||||
_hist = q(f"""
|
||||
WITH fin AS (
|
||||
SELECT year, hcpcs,
|
||||
work_rvu + coalesce(non_fac_pe_rvu, fac_pe_rvu) + mp_rvu
|
||||
AS total_rvu
|
||||
FROM pfs.rvu
|
||||
WHERE (mod IS NULL OR mod = '') AND year >= 2015
|
||||
AND hcpcs IN ({_codes_sql})
|
||||
QUALIFY row_number() OVER (PARTITION BY hcpcs, year ORDER BY hcpcs) = 1
|
||||
),
|
||||
prop AS (
|
||||
SELECT 2027 AS year, hcpcs,
|
||||
work_rvu + coalesce(non_fac_pe_rvu, fac_pe_rvu) + mp_rvu
|
||||
AS total_rvu
|
||||
FROM pfs.rvu_proposed
|
||||
WHERE cms_rule_id = 'CMS-1848-P' AND (mod IS NULL OR mod = '')
|
||||
AND hcpcs IN ({_codes_sql})
|
||||
QUALIFY row_number() OVER (PARTITION BY hcpcs ORDER BY hcpcs) = 1
|
||||
)
|
||||
SELECT * FROM fin UNION ALL SELECT * FROM prop
|
||||
ORDER BY hcpcs, year
|
||||
""")
|
||||
|
||||
_long = (
|
||||
_hist.filter(pl.col("total_rvu").is_not_null())
|
||||
.sort("hcpcs", "year")
|
||||
.with_columns(
|
||||
(
|
||||
100
|
||||
* (pl.col("total_rvu") / pl.col("total_rvu").shift(1).over("hcpcs") - 1)
|
||||
)
|
||||
.round(2)
|
||||
.alias("rvu_yoy_pct")
|
||||
)
|
||||
.join(family_pay.select("hcpcs", "family"), on="hcpcs")
|
||||
)
|
||||
|
||||
family_heatmap = (
|
||||
alt.Chart(_long.to_pandas())
|
||||
.mark_rect(stroke="#F7F5F0", strokeWidth=1)
|
||||
.encode(
|
||||
x=alt.X("year:O", title="Year (2027 = proposed)"),
|
||||
y=alt.Y("hcpcs:N", title=None),
|
||||
color=alt.Color(
|
||||
"rvu_yoy_pct:Q",
|
||||
title="RVU YoY %",
|
||||
scale=alt.Scale(domainMid=0, range=[AMBER, "#EDEBE6", TEAL]),
|
||||
),
|
||||
tooltip=[
|
||||
"hcpcs", "family", "year:O",
|
||||
alt.Tooltip("total_rvu:Q", format=".2f"),
|
||||
alt.Tooltip("rvu_yoy_pct:Q", format="+.2f"),
|
||||
],
|
||||
)
|
||||
.properties(
|
||||
title="Total-RVU Year-over-Year % — RFI Code Families, 2015-2027p",
|
||||
width=560,
|
||||
height=16 * _long["hcpcs"].n_unique(),
|
||||
)
|
||||
)
|
||||
mo.vstack(
|
||||
[
|
||||
family_heatmap,
|
||||
mo.md("""
|
||||
First priced year per code is blank (no prior year). Late
|
||||
first-appearances are themselves informative: PCM
|
||||
(99424-7) enters in 2022, APCM (G0556-8) in 2025 — the
|
||||
between-visit care infrastructure the RFI's part (d)
|
||||
builds on is recent, which is part of why CMS is asking
|
||||
what the *next* care-management construct should require.
|
||||
"""),
|
||||
]
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
# ── 3. Provenance ──────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(fr_md, mo, proposed_for):
|
||||
from datetime import date
|
||||
|
||||
_prop = proposed_for(2027)
|
||||
_close = _prop.comment_close
|
||||
_today = date.today()
|
||||
_open = _today <= _close
|
||||
_status = "OPEN" if _open else "CLOSED"
|
||||
|
||||
mo.callout(
|
||||
mo.md(f"""
|
||||
**Comment period: {_status}** — {_prop.cms_rule_id}
|
||||
({fr_md(_prop.federal_register_citation)}) published
|
||||
{_prop.published.isoformat()}, comments due
|
||||
**{_close.isoformat()}** (`proposed_for(2027).comment_close`,
|
||||
read live; today is {_today.isoformat()}). This notebook's
|
||||
exhibits and the PRISMA evidence base feed the P44 comment to
|
||||
docket CMS-2026-2377.
|
||||
"""),
|
||||
kind="success" if _open else "danger",
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo, q):
|
||||
provenance = q("""
|
||||
SELECT run_id, ingested_at, module, table_name, rule_id,
|
||||
source_file, sha256, rows, fr_citation, pincite_key
|
||||
FROM cms.ingest_log
|
||||
WHERE table_name IN ('pfs.rvu', 'pfs.rvu_proposed')
|
||||
ORDER BY ingested_at DESC
|
||||
""")
|
||||
mo.vstack(
|
||||
[
|
||||
mo.md("""
|
||||
## 3. Provenance
|
||||
|
||||
Ingest-log rows for the lake tables Section 2 reads, plus
|
||||
the evidence-base pipeline: PubMed identification —
|
||||
`dev/scripts/search_pubmed_palliative_rfi.py` (E-utilities,
|
||||
relevance-capped, run 2026-08-18); screening — `stack
|
||||
prisma screen palliative-rfi` with the project's Zotero
|
||||
criteria anchors; flow — `prisma.flow` over live Zotero
|
||||
tags. `cms.primary_care_service_code` is the aco module's
|
||||
attribution reference list (P39), not an FR ingest.
|
||||
"""),
|
||||
mo.ui.table(
|
||||
provenance.to_pandas(),
|
||||
label="Ingest Log — Lake Tables Used in Section 2",
|
||||
),
|
||||
]
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run()
|
||||
Reference in New Issue
Block a user