Define-Variables Skill

Map each exposure, outcome, covariate, and eligibility variable to a canonical guideline/consensus definition, cross-check it against prior operationalizations in comparable cohorts, then map it to the available DB variables. Call after /design-study (and /search-lit), before /write-protocol.

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 the 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 most common observational-study error: assuming a column code (status == 0, grade == 4) means what it intuitively reads like, when the codebook says otherwise.

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.

Recommend committing a DICTIONARY_FIRST_POLICY.md at the project root (or shared-config path) with the canonical dictionary path and the escalation contact.

Exit gate: before Tier 1, cross-check every row's Dict. sheet & row and Dict. verbatim against the source dictionary; no DB-backed row may be left blank.

Tier 1 — Canonical index lookup (no API calls)

Look the variable up in references/common_definitions.md (hepatology, metabolic/endocrine, renal, pulmonary, cardiovascular, oncology/imaging incidentalomas, alcohol exposure). On a hit, record the guideline, year, canonical cutoff, and 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 — never a general sweep, which buries the signal. Query pattern:

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

Stop searching a variable once the first 1-2 papers converge on the same definition. If more than five variables need Tier 2, list them and confirm with the user before running the rest.

Tier 3 — Verification

Every definition, cutoff, and era anchor must come from a verified source — a clinical guideline, a peer-reviewed paper with DOI, or an established registry data dictionary. Never take a phenotype threshold from the model's prior or a reference from memory. Before finalizing, run /verify-refs on the accumulated BibTeX to confirm every citation exists in PubMed/CrossRef. A choice with no canonical source is flagged Ad-hoc: yes, justified in 1-2 sentences, and confirmed by the user before it propagates into /write-protocol or /analyze-stats.

Output Template

Write {project_root}/variable_operationalization.md (or the path the user specifies) from 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 under the same condition
   • Canonical source: BibTeX key (e.g., @rinella2023_aasld_masld), so downstream skills can re-verify
   • Definition: one line, verbatim from the 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 the 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

Out of scope: statistical analysis → /analyze-stats; manuscript drafting → /write-paper; data cleaning / missingness → /clean-data; sample size → /calc-sample-size.

Failure Modes to Avoid

1. Column-first framing — starting from what columns exist, then picking a definition that matches. Always flip: definition first, then map. Tier 0 still applies once a column is picked: quote its codebook entry verbatim before using its values.
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. 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.