Reject Bad Sex

Review ask

As a clinical trial statistician or programmer, challenge anything in this benchmark: describe a problem in Comments, or propose a fix with the Edit button on any source file.

Task: Is this a specification error a study team could realistically make, and should the build stop on it rather than continue?
Inputs: Do they trigger the failure for the reason the summary gives?
Expected failure: Is this the right error, raised at the right point, and would it tell the programmer what to fix?

The other tabs beside Comments are background for assessment, not under review.

Summary

EditUnder reviewWhat this benchmark means

README.md

Goal: carry the recorded sex (SEX) for each subject from collected demographics, accepting only M, F, and U.

Input: collected demographics with study and subject identifiers and recorded sex (SEX).

Variables:

SEX is the subject's recorded sex, copied from collected SEX. A missing value remains missing and passes the final check; any other value must be M, F, or U.

A non-missing code outside the accepted set is rejected after the dataset is completed, so no artifact is accepted.

How to fix

Confirm the code against the study's sex coding convention. Correct X at the governed source when it is a data-entry error. If X is an intentional study code, document that policy and add it to the anchored accepted set on the SEX column:

verifications:
  - matches:
      pattern: '^[MFUX]$'

Expected failure

Editthe run is rejected; no artifact is accepted

expected/error.yaml

phase: verification
condition: matches_failed
requirement: REQ-0379

expected/error.yaml

phase: verification
condition: matches_failed
spec_paths:
  - columns.SEX.verifications[0].matches
requirement: REQ-0379
context:
  column: SEX
  failure_count: 1
  keys:
    - STUDYID: PILOT7
      USUBJID: P7-952

YAML specification

1 spec file

spec.yaml

Edit29 lines

1schema_version: "1.0"2domain: ADSL3keys: [STUDYID, USUBJID]4input:5  DM: input/dm.csv67output:8  path: adsl.csv9  columns: [STUDYID, USUBJID, SEX]1011columns:12  - name: STUDYID13    type: str14    label: Study Identifier15    derivation: DM.STUDYID1617  - name: USUBJID18    type: str19    label: Unique Subject Identifier20    derivation: DM.USUBJID2122  - name: SEX23    type: str24    label: Sex25    derivation: DM.SEX26    # A missing sex passes; any other value must be exactly M, F or U.27    verifications:28      - matches:29          pattern: '^[MFU]$'

Inputs

Under review1 source file

input/dm.csv

Edit2 rows

input/dm.csv
STUDYID	USUBJID	SEX
PILOT7		M
PILOT7		X

Unexpected Output

Under review2 rows presented to the failing check - not an accepted artifact

expected/adsl.csv

Edit2 rows

expected/adsl.csv
STUDYID	USUBJID	SEX
PILOT7		M
PILOT7		X

Shaded columns are derived— = missing value

Comments and assessment materials

Dataset Name	Variable Name	Variable Label	Type	Computational Method	Origin
ADSL	STUDYID	Study Identifier	character	Copy value from DM.STUDYID.	Predecessor
ADSL	USUBJID	Unique Subject Identifier	character	Copy value from DM.USUBJID.	Predecessor
ADSL	SEX	Sex	character	Copy value from DM.SEX.	Predecessor

Version	Date	Author	Description
1.3	2026-09-29	yamaa docs build	Show study codelist names, external dictionaries, and ISO 8601 formats in variable rows; list the document's codelist values and metadata
1.2	2026-09-29	yamaa docs build	Classify ADaM copies from a declared input as Predecessor and recodes as Derived, and SDTM recodes of locally derived columns as Derived
1.1	2026-09-29	yamaa docs build	Resolve lookups into the donor record they select, and give every derivation shape a plain-language rule instead of its raw YAML
1.0		yamaa docs build	Initial generation from spec.yaml

How to fix

input/dm.csv

expected/adsl.csv

Comments

Mapping spec