define-variables

name	define-variables
description	Literature-grounded variable operationalization for observational research. Turns a data dictionary + research question into a citation-backed table of exposure/outcome/covariate definitions, cutoffs, and DB variable mappings. Prevents ad-hoc phenotype definitions that invite reviewer rejection. Bridges /search-lit output into /write-protocol Methods.
triggers	variable definition, phenotype definition, operationalization, cutoff justification, inclusion criteria, case definition, grouping criteria, literature-grounded definition, canonical definition, 변수 정의, 정의 근거
tools	Read, Write, Edit, Bash, Grep, Glob
model	inherit

Define-Variables Skill

Purpose

Every observational study operationalizes abstract constructs (MASLD, CKD, emphysema, obesity, incidentaloma) into concrete rules against the available data dictionary. When that operationalization is invented ad-hoc from the dictionary alone, reviewers reject on construct validity regardless of downstream statistics.

This skill forces a literature-first pass: each variable is mapped to a canonical guideline/consensus definition, cross-checked against prior operationalizations in comparable cohorts, then mapped to available DB variables. Ad-hoc deviations are flagged explicitly and justified, not hidden.

Use it when:

• a study question is known and variables are being selected
• inclusion/exclusion criteria or phenotype definitions need citation backing
• a data dictionary has ambiguous or derived variables (eGFR formula, BMI class, liver steatosis criteria, etc.)
• a reviewer asked "why this cutoff?"
• a retrospective audit reveals drifted definitions across projects in the same cohort

Call after /design-study, before /write-protocol.

Communication Rules

• Communicate in the user's preferred language.
• All variable names, guideline names, cutoffs in English.
• Produce one artifact: variable_operationalization.md in the project root (or path the user specifies).

Inputs

1. Research question (one sentence)
2. Candidate variables — exposure, outcome, key covariates, eligibility filters
3. Data dictionary path (xlsx / csv / markdown) OR explicit list of available DB columns
4. Cohort type (e.g., health-screening, NHANES-like, claims, registry) — informs which prior-art cohort to compare against

Missing inputs → ask once, then proceed.

4-Tier Pipeline (DB codebook + token-efficient literature)

Tier 0 — DB codebook lookup (mandatory for DB-backed observational studies)

Trigger: project has a project.yaml::db.dictionary_path field pointing to a machine-readable codebook (xlsx/csv/markdown), OR user supplied a dictionary path in inputs. If neither, skip to Tier 1.

For every candidate DB variable — before touching literature — open the dictionary and record, verbatim, the sheet name, row number, and code→meaning mapping. This prevents the single most common observational-study error: assuming a column code (status == 0, grade == 4) means what it intuitively reads like, when the codebook says otherwise.

Concrete procedure per variable:

1. Locate the variable in the dictionary by exact column name.
2. Copy verbatim: the sheet title, row number, and full code→meaning mapping (or unit/range statement for continuous vars).
3. Paste into the Dict. sheet & row + Dict. verbatim columns of the operationalization table.
4. If the variable is not found, OR the codebook is silent on a specific code value, file a question to the DB owner / data steward. Do NOT infer from cross-tabs, do NOT guess, do NOT proceed with that variable until a verbatim answer exists.

Empirical checks (value distributions, cross-tabs with related columns) are useful for sanity testing after the verbatim codebook meaning is recorded — never as a substitute for it.

Project-level binding (recommended): commit a DICTIONARY_FIRST_POLICY.md at the project root (or shared-config path) capturing the canonical dictionary path + escalation contact. Cross-project rule template: ~/.claude/rules/dictionary-first.md.

Exit gate: check_dictionary_citations.py (or equivalent) PASS on the operationalization table before running Tier 1.

Tier 1 — Canonical index lookup (no API calls)

Check references/common_definitions.md (shipped with skill) for the variable. Covers high-frequency constructs:

• Liver: MASLD (AASLD 2023), MetALD (AASLD 2023), MAFLD (2020), NAFLD (legacy), ALD, viral hepatitis (AASLD 2022/2024 HBV, AASLD-IDSA HCV)
• Metabolic: T2DM (ADA 2024), prediabetes (ADA 2024), metabolic syndrome (IDF 2009 / NCEP ATP III / K-NCEP), obesity/BMI (WHO Asian 2004 + WHO global), HTN (ACC/AHA 2017 + JNC-8), dyslipidemia (NCEP ATP III, 2023 AHA/ACC)
• Renal: CKD (KDIGO 2024), eGFR formulas (CKD-EPI 2021 race-free, MDRD legacy), incidental renal mass (ACR 2018 white paper, Bosniak 2019)
• Pulmonary: COPD (GOLD 2024), emphysema imaging (Fleischner 2015)
• CV: CAC scoring (Agatston 1990, MESA percentiles), CAD risk (2018 ACC/AHA cholesterol, PREVENT 2023)
• Cancer: gastric cancer H. pylori (Maastricht VI 2022), thyroid nodule (ACR TI-RADS 2017), gallbladder polyp (European 2022 joint guideline)
• Imaging incidentalomas: adrenal (ACR 2023), pancreas (ACR 2017), renal (ACR 2018), thyroid (ACR 2017)

If the variable hits Tier 1, record: guideline, year, canonical cutoff, BibTeX key. Done — no /search-lit call.

Tier 2 — Targeted /search-lit (focused queries only)

For variables NOT in Tier 1, OR when subgroup justification is needed (Asian-specific cutoff, pediatric, young-adult, pregnancy, etc.), call /search-lit with one query per variable — not a general sweep. Query pattern:

"{construct} definition {cohort type} {subgroup qualifier}"
e.g., "obstructive sleep apnea prevalence Korean health screening cohort"

Cap: 5 queries per session. Stop early if first 1-2 papers converge on the same definition.

Tier 3 — Verification

Before finalizing, run /verify-refs on the accumulated BibTeX to confirm every citation exists in PubMed/CrossRef. Ad-hoc choices (no canonical source found) must be flagged Ad-hoc: yes and justified with 1-2 sentences — never hidden.

Output Template

Write to {project_root}/variable_operationalization.md using templates/variable_operationalization.md. Required structure:

1. Header: research question, cohort type, date, author

2. Operationalization table — one row per variable:

   | Variable | Role | Dict. sheet & row | Dict. verbatim | Canonical source | Definition | Cutoff | DB vars | Implementation | Ad-hoc? |

   • Role: exposure / outcome / covariate / eligibility
   • Dict. sheet & row: e.g. 5-1.복부초음파 r12 — mandatory if a DB dictionary exists
   • Dict. verbatim: full code→meaning string copied from the dictionary — mandatory same condition
   • Canonical source: BibTeX key (e.g., @rinella2023_aasld_masld)
   • Definition: one line, verbatim from guideline where possible
   • Cutoff: numeric + units
   • DB vars: exact dictionary column names used
   • Implementation: SQL/pandas-style pseudocode (e.g., bmi>=25 & (b_tg>=150 | b_hdl<40))
   • Ad-hoc?: yes/no. If yes, justification below table

3. Ad-hoc justifications — for each yes row

4. Mapping gaps — variables in the protocol with no DB equivalent; list proxy / omit / request decisions

5. References — BibTeX block

Non-Goals

• Statistical analysis → /analyze-stats
• Manuscript drafting → /write-paper
• Data cleaning / missingness → /clean-data
• Sample size → /calc-sample-size

Pipeline Position

intake-project → design-study → search-lit → define-variables → write-protocol → analyze-stats → write-paper
                                              ^^^^^^^^^^^^^^^

/orchestrate should insert this skill between /search-lit and /write-protocol for any observational cohort or registry study.

Anti-Hallucination

Every variable definition, cutoff, and era anchor must be grounded in a verified source — a clinical guideline, a peer-reviewed paper with DOI, or an established registry data dictionary. Never invent a phenotype threshold from the model's prior; if the source is unknown, mark the row Ad-hoc: yes and require user confirmation before it propagates into /write-protocol or /analyze-stats. When citing papers to justify a cutoff, verify the citation via /search-lit or /verify-refs — do not carry references from memory alone. The output table must carry explicit source, year, and guideline_version columns so downstream skills can re-verify.

Failure Modes to Avoid

0. Ad-hoc DB code interpretation (the single most costly observational-study error). Interpreting a column value (status == 0, grade == 4) by its surface reading without consulting the codebook. Tier 0 exists specifically to prevent this. Distinguish from Failure #1: Tier 0 says "once you've picked the DB column, quote the codebook verbatim before using its values." Failure #1 says "don't pick DB columns before picking definitions from literature." Both rules co-exist.
1. Dictionary-first framing — starting from what columns exist, then picking a definition that matches. Always flip: definition first, then map.
2. Cutoff drift — using a different cutoff than the cited guideline without justification (e.g., BMI≥23 cited as WHO Asian while text says ≥25).
3. Mixing eras — 2020 MAFLD criteria with 2023 MASLD criteria in the same analysis. Pick one and note why.
4. Silent ad-hoc — introducing a novel cutoff without the Ad-hoc: yes flag.
5. Sweep-style /search-lit — running a generic lit search instead of one focused query per gap variable. Wastes tokens and buries the signal.
6. Dose/duration structural-missingness — operationalizing a dose/duration covariate (pack-years, cessation-years, alcohol grams/week) anchored to a categorical exposure (smoking status, alcohol use) without specifying what the reference level (never-smoker, never-drinker) does to the dose. A never-smoker's pack-years is a structural zero, not a missing value; conflating the two collapses the analytic sample under complete-case modeling and lets MICE fabricate a non-zero dose for the unexposed. Operationalize it explicitly — add a row with Role = covariate and Implementation = "IF status == 'never' THEN dose = 0 ELSE measured_value" — and adjust on the categorical status variable, reserving the continuous dose for an exposed-only secondary analysis. /clean-data (categorical-implied-zero flag) and /analyze-stats ("Covariate Pitfalls") enforce this downstream.