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Summary

Lifecycle: reviewedEditUnder review
What this benchmark means
README.md

Public entries: Python and R. Passing coverage requires an installed inventory.

Goal: standardize the free text a site enters for each adverse event (the reported term and the causality) and pull the reference number out of the sponsor's identifier.

Input: the site's AE records (input/ae.csv) carrying the collected AESPID, AETERM, and AEREL.

Variables:

  • AEREFNUM is the number taken from AESPID values shaped like AE-001; a missing identifier gives 0 and any other shape gives -1.
  • AETERMLO is the reported term in lower case.
  • AERELLC is the causality in lower case; a missing causality gives not reported.
  • AREL is the analysis causality in upper case: RELATED, POSSIBLY RELATED, NOT RELATED, or NOT REPORTED.

Note: the identifier pattern accepts exactly three digits, so AE-000 gives 0 (the same value a missing identifier gives), while AE-1000 and a lowercase ae-007 each give -1.

Inputs

Under review
1 source file

input/ae.csv

Edit
10 rows
input/ae.csv
STUDYIDUSUBJIDAESEQAESPIDAETERMAEREL
CATH-011AE-001HeadacheRELATED
CATH-012—HEADACHEPossibly Related
CATH-013BAD-IDNausea—
CATH-011AE-104FatigueNOT RELATED
CATH-012—DizzinessNOT RELATED
CATH-013AE-999hEADACHErelated
CATH-011AE-000VomitingNOT RELATED
CATH-012AE-1000Abdominal PainPossibly Related
CATH-013ae-007Rash—
CATH-014AE-007PruritusNot Related

Expected output

Under review
10 expected rows

expected/adae.csv

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10 rows
expected/adae.csv
STUDYIDUSUBJIDAESEQAESPIDAEREFNUMAETERMAETERMLOAERELLCAREL
CATH-011AE-0011HeadacheheadacherelatedRELATED
CATH-012—0HEADACHEheadachepossibly relatedPOSSIBLY RELATED
CATH-013BAD-ID-1Nauseanauseanot reportedNOT REPORTED
CATH-011AE-104104Fatiguefatiguenot relatedNOT RELATED
CATH-012—0Dizzinessdizzinessnot relatedNOT RELATED
CATH-013AE-999999hEADACHEheadacherelatedRELATED
CATH-011AE-0000Vomitingvomitingnot relatedNOT RELATED
CATH-012AE-1000-1Abdominal Painabdominal painpossibly relatedPOSSIBLY RELATED
CATH-013ae-007-1Rashrashnot reportedNOT REPORTED
CATH-014AE-0077Prurituspruritusnot relatedNOT RELATED
Shaded columns are derived— = missing value

Human review and assessment

Your review

3 questions and your comments · View benchmark discussions

Mapping spec

Generated from the benchmark YAML; do not edit by hand

Dataset NameVariable NameVariable LabelTypeCodelist/Controlled TermsCoreComputational MethodOrigin
ADAESTUDYIDStudy IdentifiercharacterCopy value from AE.STUDYID.Predecessor
ADAEUSUBJIDUnique Subject IdentifiercharacterCopy value from AE.USUBJID.Predecessor
ADAEAESEQSequence NumbernumericCopy value from AE.AESEQ.Predecessor
ADAEAESPIDSponsor-Defined IdentifiercharacterCopy value from AE.AESPID.Predecessor
ADAEAEREFNUMAdverse Event Reference NumbernumericExtract capture group 1 matching ^AE-([0-9]{3})$ from AESPID; a missing source → "0"; no match → "-1".Derived
ADAEAETERMReported Term for the Adverse EventcharacterCopy value from AE.AETERM.Predecessor
ADAEAETERMLOReported Term in Lower CasecharacterConvert AETERM to lower case.Derived
ADAEAERELLCCausality in Lower CasecharacterConvert AE.AEREL to lower case; a missing source → "not reported".Derived
ADAEARELAnalysis CausalitycharacterRELATED, POSSIBLY RELATED, NOT RELATED, NOT REPORTEDConvert AERELLC to upper case.Derived
VariablePermitted values
ARELRELATED, POSSIBLY RELATED, NOT RELATED, NOT REPORTED
VersionDateAuthorDescriptionReviewerSign-off
1.72026-10-20yamaa docs buildRead a specification in the 1.0 surface as its 0.5 counterpart; state that missing values stay missing where a mapping declares no missing handler, and leave out a declared missing answer that restates a handler's default
1.62026-10-17yamaa docs buildDescribe collected date parts written as ISO 8601 date text and times of day counted in seconds since midnight
1.52026-10-17yamaa docs buildName collected precision in words instead of letter codes; state imputation fallbacks, minimum source precision, bounds ignored outside the collected period, and invalid text with no handler as an error; describe date and datetime shifts, imputation flag codes, and partial text giving no date part
1.42026-10-15yamaa docs buildState datetime precision codes through the hour and minute, and complete only the time parts a collected datetime lacks
1.32026-09-29yamaa docs buildShow study codelist names, external dictionaries, and ISO 8601 formats in variable rows; list the document's codelist values and metadata
1.22026-09-29yamaa docs buildClassify ADaM copies from a declared input as Predecessor and recodes as Derived, and SDTM recodes of locally derived columns as Derived
1.12026-09-29yamaa docs buildResolve lookups into the donor record they select, and give every derivation shape a plain-language rule instead of its raw YAML
1.0yamaa docs buildInitial generation from spec.yaml

Agent prompt

The request an agent receives with the input files; each tier states no more than the one above

prompts/adam-adae-text-cleanup/full.md

Edit
16 lines

Following CDISC ADaM standards, use the provided AE dataset to create an ADAE dataset with one record per adverse event.

The output dataset should contain the following columns in this order: STUDYID, USUBJID, AESEQ, AESPID, AEREFNUM, AETERM, AETERMLO, AERELLC, AREL

AEREFNUM is the number taken from AESPID when AESPID consists of "AE-" followed by exactly three digits, so "AE-001" gives 1. A missing AESPID gives 0, and an AESPID of any other shape gives -1. The pattern accepts exactly three digits, so "AE-000" gives 0, while "AE-1000" and a lower-case "ae-007" each give -1. AETERMLO is AETERM in lower case. AERELLC is AEREL in lower case, and a missing AEREL gives "not reported". AREL is AERELLC in upper case: "RELATED", "POSSIBLY RELATED", "NOT RELATED", or "NOT REPORTED".

Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.

prompts/adam-adae-text-cleanup/conventions.md

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10 lines

Following CDISC ADaM standards, use the provided AE dataset to create an ADAE dataset with one record per adverse event.

The output dataset should contain the following columns in this order: STUDYID, USUBJID, AESEQ, AESPID, AEREFNUM, AETERM, AETERMLO, AERELLC, AREL

AREL is "RELATED", "POSSIBLY RELATED", "NOT RELATED", or "NOT REPORTED".

Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.

prompts/adam-adae-text-cleanup/brief.md

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8 lines

Following CDISC ADaM standards, use the provided AE dataset to create an ADAE dataset with one record per adverse event.

The output dataset should contain the following columns in this order: STUDYID, USUBJID, AESEQ, AESPID, AEREFNUM, AETERM, AETERMLO, AERELLC, AREL

Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.

yamaa code

Public calls generated from benchmark.yaml; results must match the expected output

R

Edit
3 lines
library(yamaa)yamaa_spec("spec.yaml")$run()$output

Python

Edit
3 lines
import yamaayamaa.spec("spec.yaml").run().output

Solution without yamaa

Written from the full prompt and the inputs alone; CI checks it reproduces the expected output

solutions/adam-adae-text-cleanup/result.R

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34 lines
# Reference solution for the yamaa benchmark adam-adae-text-cleanup (R track).## Written from the benchmark's full prompt and its inputs alone, as an agent# would write it. Harbor's oracle agent runs it to check that the prompt can# be solved and that the grader scores a correct answer 1.library(dplyr, warn.conflicts = FALSE)library(readr)library(stringr)ae <- read_csv(  "/app/input/ae.csv",  col_types = cols(AESEQ = col_integer(), .default = col_character()))adae <- ae |>  mutate(    # AE, a hyphen, and exactly three digits; missing is 0, any other shape    # is -1, and the digits give the number.    AEREFNUM = case_when(      is.na(AESPID) | AESPID == "" ~ 0L,      str_detect(AESPID, "^AE-[0-9]{3}$") ~ suppressWarnings(as.integer(str_sub(AESPID, 4, 6))),      .default = -1L    ),    AETERMLO = tolower(AETERM),    AERELLC = if_else(is.na(AEREL) | AEREL == "", "not reported", tolower(AEREL)),    AREL = toupper(AERELLC)  ) |>  select(    STUDYID, USUBJID, AESEQ, AESPID, AEREFNUM, AETERM, AETERMLO, AERELLC, AREL  )dir.create("/app/output", showWarnings = FALSE)write_csv(adae, "/app/output/adae.csv", na = "")

solutions/adam-adae-text-cleanup/result.py

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68 lines
# Reference solution for the yamaa benchmark adam-adae-text-cleanup (Python track).## Written from the benchmark's full prompt and its inputs alone, as an agent# would write it. Harbor's oracle agent runs it to check that the prompt can# be solved and that the grader scores a correct answer 1.import csvimport reINPUT = "/app/input/ae.csv"OUTPUT = "/app/output/adae.csv"COLUMNS = [    "STUDYID",    "USUBJID",    "AESEQ",    "AESPID",    "AEREFNUM",    "AETERM",    "AETERMLO",    "AERELLC",    "AREL",]REFNUM_PATTERN = re.compile(r"^AE-(\d{3})$")def aerefnum(aespid):    if aespid is None or aespid == "":        return 0    match = REFNUM_PATTERN.match(aespid)    if match:        return int(match.group(1))    return -1def main():    with open(INPUT, newline="", encoding="utf-8") as handle:        rows = list(csv.DictReader(handle))    out_rows = []    for row in rows:        aespid = row.get("AESPID") or ""        aeterm = row.get("AETERM") or ""        aerel = row.get("AEREL") or ""        aerellc = aerel.lower() if aerel != "" else "not reported"        out_rows.append(            {                "STUDYID": row.get("STUDYID") or "",                "USUBJID": row.get("USUBJID") or "",                "AESEQ": row.get("AESEQ") or "",                "AESPID": aespid,                "AEREFNUM": aerefnum(aespid),                "AETERM": aeterm,                "AETERMLO": aeterm.lower(),                "AERELLC": aerellc,                "AREL": aerellc.upper(),            }        )    with open(OUTPUT, "w", newline="", encoding="utf-8") as handle:        writer = csv.DictWriter(handle, fieldnames=COLUMNS)        writer.writeheader()        writer.writerows(out_rows)if __name__ == "__main__":    main()

solutions/adam-adae-text-cleanup/result.sas

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25 lines
/* opensas reference solution for the yamaa benchmark adam-adae-text-cleanup.   Reads the benchmark inputs and derives the requested output.   Execute with the existing opensas runtime: opensas /app/output/result.sas. */data ae;  length STUDYID USUBJID AESPID AETERM AEREL $1024;  infile "/app/input/ae.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;  input STUDYID : $char1024. USUBJID : $char1024. AESEQ AESPID : $char1024. AETERM : $char1024. AEREL : $char1024.;run;data result;  length AETERMLO AERELLC AREL $1024;  set ae;  if missing(AESPID) then AEREFNUM=0;  else if prxmatch('/^AE-[0-9]{3}$/',strip(AESPID)) then AEREFNUM=input(substr(AESPID,4,3),best32.);  else AEREFNUM=-1;  AETERMLO=lowcase(AETERM); AERELLC=lowcase(AEREL);  if missing(AEREL) then AERELLC='not reported';  AREL=upcase(AERELLC);run;proc sql;  create table final as select STUDYID, USUBJID, AESEQ, AESPID, AEREFNUM, AETERM, AETERMLO, AERELLC, AREL from result;quit;proc export data=final outfile="/app/output/adae.csv" dbms=csv replace; run;