VibeRounds — Clinical Intelligence Suite companion module
[!IMPORTANT] Disclaimer & Independent Verification Required This module produces learning observations and exploratory analysis plans, not validated clinical evidence. Any correlation, “replication,” or “variation” the AI reports between a published paper and your cohort’s deterministic analytics must be independently checked against the actual VibeRounds output numbers before being treated as a finding. The AI never sees patient-level data — it only ever sees paper text, module names/descriptions, and the numeric summaries you paste back to it. Synthetic cohorts (default
seed: 42) are for methodology rehearsal only; findings from synthetic data have no clinical meaning.
[!NOTE] What this module is and is not This is a bridge module: it sits between the literature and the Suite’s 100+ deterministic analytics buttons. It does not run any analytics itself. It reads a paper, tells you which existing Suite module(s) most closely probe the same question, drafts the cohort filter and sequence of buttons to click, and — once you paste back the real output — tells you whether your cohort’s numbers point the same direction as the paper or diverge, and by how much. Every number the AI reasons about must come from the deterministic Suite output, never from its own memory of “typical” clinical values.
Given a research paper (PDF upload, DOI/link, or pasted abstract/full text), produce:
Backward factors, Time-to-escalation, Co-occurrence, Cox proportional hazards · V2) that could probe an analogous question on your own cohort.Reach for this module when:
Backward hypothesis/Forward hypothesis/Closed loop modules and need a literature-sourced hypothesis to seed them with.Do not reach for this module to:
Initiation (Steps RP.0–RP.1) → Execution (Steps RP.2–RP.4) → Closure / Review (Steps RP.5–RP.6)
Before any extraction, tell the AI what you’re giving it and what you want out of the session.
Prompt: “I’m giving you a research paper as [PDF upload / link / pasted text]. I’m using it alongside VibeRounds, a deterministic clinical-analytics suite with cohort filtering and ~100 named analytic modules (list below or attached). My goal for this session is: [explore a hypothesis / sanity-check a published finding on my own data / seed a Backward-hypothesis run / just extract structured insight, no pipeline needed]. Do not fetch anything beyond what I give you. If the link is inaccessible or the PDF didn’t parse, tell me plainly instead of guessing at the paper’s content.”
Application Note: If using an LLM with browsing (e.g., Gemini in-browser as in the screenshots), a link can be fetched live; if using a model without browsing, paste the abstract + methods + results tables directly, since guessed content from a paper the model hasn’t actually read is the single biggest failure mode of this module.
Prompt: “Extract the following from the paper, and mark any field ‘not stated’ rather than inferring it:
- Population — sample size, inclusion/exclusion criteria, setting.
- Exposure / index condition / comparator groups.
- Primary outcome(s) and how each was measured/defined.
- Key effect estimate(s) — e.g., hazard ratio, odds ratio, absolute rate difference — with confidence intervals if given.
- Study design (retrospective cohort, RCT, case-control, registry analysis, etc.) and its position on the evidence hierarchy.
- Named limitations the authors themselves state (not ones you infer).
- One-sentence plain-English summary of the central claim. Present this as a table. Do not add interpretation yet.”
Application Note: Keeping extraction and interpretation as separate steps is deliberate — it lets you (the human) catch a bad extraction before the AI starts building an analysis plan on top of it.
Ground truth — Suite Button Reference (as of this cohort’s Suite screen). This is the closed set the model may map to. If your Suite version differs (buttons added/removed/renamed), replace this table before running the module — do not let the model reason from memory of a prior version.
| Section | Buttons |
|---|---|
| Overview | Cohort overview · Data quality assessment (v1) · Trajectory map · patient-journey-map · V3 · Cohort overview by age band · Index case comparison |
| Process Mining | pathway-discovery · V3 · timing-intervals · V3 · trajectory-clusters · V3 · trajectory-association-rules · V3 · trajectory-outliers · V3 |
| Discovery & Replication | Backward lift · backward-lift-coded · V2 · Backward factors · Forward hypothesis · Closed loop |
| Clinical / Diagnostic | Co-occurrence · Phenotype clusters · phenotype-clusters-coded · V2 · Polypharmacy · Time-to-escalation · Escalation-free survival (KM) · Symptom proximity · Medication co-prescription · med-combos-coded · V2 · Symptom co-occurrence · Symptom → Investigation · LOS distribution · LOS / disposition · Test-ordering intensity · Hub network · Pathway frequencies · V2 · Treatment pattern · Refill gap · Signal detection · Signal detection (coded) · V2 · Comorbidity prevalence (coded) · V2 · Medication class (coded) · V2 · Symptom patterns (coded) · V2 |
| Public Health / Population | Readmission drivers · Equity check · equity-coded · V2 · High-utilizers · SDOH · Screening coverage · Payer mix · 90-day encounters vs outcomes · Pharmacovigilance · Surveillance · A vs. B drift · Region × insurance · Age × sex × subgroup · Seasonal pattern · Vaccination status · Signal detection (coded) · V2 · Signal detection (temporal) · V3 |
| Medication / Longitudinal | Medication sequences · V3 · Medication changes · V3 · Medication → outcome (lagged) · V3 · Unexpected medication patterns · V3 |
| Visual / Statistics | Lab value distributions (box + violin) · V2 · Event series sparklines · V2 · Calendar heatmap · V2 · Comorbidity chord diagram · V2 · Cox proportional hazards · V2 · Frequent itemsets (FP-Growth) · V2 |
| Patient / Evidence | Diagnostic ambiguity · Guideline adherence · Care gaps · Symptom burden · Adherence × SDOH · Screening → escalation · Checkpoint paths · Transition matrix · Exposure → Event → Outcome timelines · Patient segments · Disease overview |
| Records | Build your own query · Patient data explorer · Knowledge graph · V2 · Cohort details · V3 · PICO/PECO Study |
Guardrail instruction (include this verbatim in your prompt): “Only map to button names that appear verbatim in the reference table above. If nothing in the table is a good match for the paper’s question, say explicitly ‘no existing module fits — closest approximation is [X], with this gap: [Y]’ or recommend
Build your own queryas a manual fallback. Never invent a button name, and never describe a real button as doing something it isn’t named for. If I’ve pasted an updated list that differs from your training, use only what I pasted.”
Prompt: “Given the paper’s population, exposure, and outcome from Step RP.1, and using only the module names in the reference table above, identify:
- The 1–3 modules that most directly probe an analogous question on a cohort (name the exact button label).
- For each, state in one sentence why it’s the right module and what output shape to expect (e.g., ‘Cox proportional hazards · V2 will return a hazard ratio table — compare its sign and rough magnitude to the paper’s HR of 1.8’).
- Flag if no existing module is a good match, and if so, whether
Build your own query(the manual cohort filter builder) could approximate it instead. Do not assume my cohort will reproduce the paper’s population — just map the question, not the expected answer.”
Application Note: This step is where the module earns its keep over a generic “summarize this paper” prompt — it forces a concrete decision (which button, in what order) rather than a vague “you could explore comorbidities.”
Prompt: “Given the mapped modules from Step RP.2, draft the cohort definition I should build in the Suite’s sidebar or
Build your own querypanel to approximate the paper’s population as closely as this dataset allows. Specify:
- Which fields/conditions to filter on (e.g., Category is Cardiometabolic; Lab includes X).
- Whether this should run as Include or as a subset comparison (matched subset vs. everyone else).
- What to do if the exact inclusion/exclusion criteria from the paper aren’t representable as fields in this cohort (state the closest approximation and name the gap explicitly — don’t silently substitute a looser filter).”
Prompt: “Lay out the exact click-by-click sequence I should follow in the Suite UI: which button first, what to note from its output before moving to the next, and which later module depends on an earlier one’s result (e.g., ‘run Cohort overview first to confirm N and confirm the filter matched a sensible subset before running Cox proportional hazards · V2’). Number the steps. If a step could be run in the Advanced Browser (SQL-like
Build your own query) instead of a preset button, say so as an alternative branch, not a replacement.”
Application Note: This is the “whole pipeline” the user asked for — a numbered sequence, not a single button. It mirrors the Suite’s own “Suggested Research Learning Path” pattern (see screenshot 2) but seeded from the paper rather than generically from the cohort’s readmission rate.
After you’ve actually run the pipeline in the Suite and have real output (numbers, tables, KM curves, etc.), come back with the results.
Prompt: “Here is the actual output from running the pipeline: [paste numeric results / describe the chart / paste table]. Compare this against the paper’s effect estimate from Step RP.1. Tell me:
- Direction: same direction as the paper, opposite, or null/non-significant here?
- Rough magnitude: in the same ballpark, meaningfully smaller/larger, or not comparable (say why if not comparable — e.g., different outcome definition, synthetic data, underpowered subset).
- Most plausible explanations for any divergence — rank 2–4 candidates (e.g., cohort composition differs, this is synthetic data with seed 42 and no real clinical signal, confounder present in the paper’s adjusted model but not in this cohort’s fields, sample size too small for the effect to show).
- What I’d need to do next to actually adjudicate between those explanations, if I wanted to (e.g., re-run with Database mode on a real registry, add a covariate, check Data quality assessment first). Do not state or imply that this constitutes replication or refutation of the paper — only that the cohort’s deterministic output does or doesn’t point the same direction.”
Prompt: “Summarize this whole session in 5 lines: the paper’s claim, the module(s) used, the cohort filter applied, what the Suite’s deterministic output showed, and the single most important caveat a reader should know before treating this as anything beyond an exploratory exercise.”
Application Note: This closing artifact is what should actually get saved (e.g., via Save suite in the Suite UI) alongside the numeric outputs — the AI’s prose summary is not itself the record; the Suite’s exported analytics are.
Paper claim: [e.g., "CKD stage ≥3 associated with HR 1.8 for 90-day readmission"]
Mapped module(s): Backward factors → Cox proportional hazards · V2
Cohort filter used: Category is Cardiometabolic AND Lab includes [creatinine marker]
Matched / total: [n] of [N] ([%])
Suite output: HR = [x], 95% CI [y–z] | KM curve separation: [yes/no]
Direction vs. paper: [same / opposite / null]
Magnitude vs. paper: [comparable / smaller / larger / not comparable — why]
Top divergence driver: [e.g., synthetic seed-42 data — no real clinical signal expected]
Next step if pursuing: [e.g., switch to Database mode with real registry, add covariate X]
Source: Synthetic is active (seed 42 or otherwise), any “concordance” with a real paper is coincidental by construction and must be labeled as such in Step RP.5 — the AI should say this unprompted if it notices the source field, and you should say it if the AI doesn’t.Build your own query fallback). It must not propose an analysis the Suite has no button for, or assume a capability (e.g., propensity-score matching, meta-analytic pooling, genomic linkage) that isn’t in the table. If the paper’s question genuinely has no Suite analog, the correct output is “not computable in this Suite” — not a best-effort improvisation dressed up as a real module.Module RP — Research Paper → Insight & Pipeline Generator. Companion to the VibeRounds Clinical Intelligence Suite. Not clinical decision support; produces exploratory, human-reviewed learning artifacts only.