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: Would a real study need this derivation, specified this way? Does it follow CDISC ADaM: variable names, controlled terms, record structure? Flag any rule that is ambiguous or that a sponsor would handle differently.
- Inputs: Do they look like data collected in a real trial, and do they include the situations you would expect to meet in one?
- Expected output: Would independent QC programming reproduce every value from these inputs and rules?
The other tabs beside Comments are background for assessment, not under review.
Summary
README.mdGoal: 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:
AEREFNUMis the number taken fromAESPIDvalues shaped likeAE-001; a missing identifier gives0and any other shape gives-1.AETERMLOis the reported term in lower case.AERELLCis the causality in lower case; a missing causality givesnot reported.ARELis the analysis causality in upper case:RELATED,POSSIBLY RELATED,NOT RELATED, orNOT 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 review1 source fileinput/ae.csv
Edit10 rows| STUDYID | USUBJID | AESEQ | AESPID | AETERM | AEREL |
|---|---|---|---|---|---|
| CATH-01 | 1 | AE-001 | Headache | RELATED | |
| CATH-01 | 2 | — | HEADACHE | Possibly Related | |
| CATH-01 | 3 | BAD-ID | Nausea | — | |
| CATH-01 | 1 | AE-104 | Fatigue | NOT RELATED | |
| CATH-01 | 2 | — | Dizziness | NOT RELATED | |
| CATH-01 | 3 | AE-999 | hEADACHE | related | |
| CATH-01 | 1 | AE-000 | Vomiting | NOT RELATED | |
| CATH-01 | 2 | AE-1000 | Abdominal Pain | Possibly Related | |
| CATH-01 | 3 | ae-007 | Rash | — | |
| CATH-01 | 4 | AE-007 | Pruritus | Not Related |
Expected output
Under review10 expected rowsexpected/adae.csv
Edit10 rows| STUDYID | USUBJID | AESEQ | AESPID | AEREFNUM | AETERM | AETERMLO | AERELLC | AREL |
|---|---|---|---|---|---|---|---|---|
| CATH-01 | 1 | AE-001 | 1 | Headache | headache | related | RELATED | |
| CATH-01 | 2 | — | 0 | HEADACHE | headache | possibly related | POSSIBLY RELATED | |
| CATH-01 | 3 | BAD-ID | -1 | Nausea | nausea | not reported | NOT REPORTED | |
| CATH-01 | 1 | AE-104 | 104 | Fatigue | fatigue | not related | NOT RELATED | |
| CATH-01 | 2 | — | 0 | Dizziness | dizziness | not related | NOT RELATED | |
| CATH-01 | 3 | AE-999 | 999 | hEADACHE | headache | related | RELATED | |
| CATH-01 | 1 | AE-000 | 0 | Vomiting | vomiting | not related | NOT RELATED | |
| CATH-01 | 2 | AE-1000 | -1 | Abdominal Pain | abdominal pain | possibly related | POSSIBLY RELATED | |
| CATH-01 | 3 | ae-007 | -1 | Rash | rash | not reported | NOT REPORTED | |
| CATH-01 | 4 | AE-007 | 7 | Pruritus | pruritus | not related | NOT RELATED |
Comments and assessment materials
| Dataset Name | Variable Name | Variable Label | Type | Codelist/Controlled Terms | Core | Computational Method | Origin |
|---|---|---|---|---|---|---|---|
| ADAE | STUDYID | Study Identifier | character | Copy value from AE.STUDYID. | Predecessor | ||
| ADAE | USUBJID | Unique Subject Identifier | character | Copy value from AE.USUBJID. | Predecessor | ||
| ADAE | AESEQ | Sequence Number | numeric | Copy value from AE.AESEQ. | Predecessor | ||
| ADAE | AESPID | Sponsor-Defined Identifier | character | Copy value from AE.AESPID. | Predecessor | ||
| ADAE | AEREFNUM | Adverse Event Reference Number | numeric | Extract capture group 1 matching ^AE-([0-9]{3})$ from AESPID; a missing source → "0"; no match → "-1". | Derived | ||
| ADAE | AETERM | Reported Term for the Adverse Event | character | Copy value from AE.AETERM. | Predecessor | ||
| ADAE | AETERMLO | Reported Term in Lower Case | character | Convert AETERM to lower case. | Derived | ||
| ADAE | AERELLC | Causality in Lower Case | character | Convert AE.AEREL to lower case; a missing source → "not reported". | Derived | ||
| ADAE | AREL | Analysis Causality | character | RELATED, POSSIBLY RELATED, NOT RELATED, NOT REPORTED | Convert AERELLC to upper case. | Derived |
| Variable | Permitted values |
|---|---|
| AREL | RELATED, POSSIBLY RELATED, NOT RELATED, NOT REPORTED |
| Version | Date | Author | Description | Reviewer | Sign-off |
|---|---|---|---|---|---|
| 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 |
prompts/adam-adae-text-cleanup/full.md
Edit18 linesFollowing 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 an AESPID made of the upper-case letters AE, a hyphen, and exactly three digits, so "AE-042" gives 42 and three zeroes give 0. A missing identifier gives 0, and any other shape, including lower-case letters or more or fewer digits, 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.
Read the source datasets from /app/input and save the completed dataset as /app/output/adae.csv.
prompts/adam-adae-text-cleanup/conventions.md
Edit13 linesFollowing 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 a number, or -1. AETERMLO is in lower case. AERELLC is in lower case, or is "not reported". AREL is the analysis causality in upper case, 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
Edit9 linesFollowing 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.
run.R
Edit4 lineslibrary(yamaa)adae <- yamaa_domain("spec.yaml")$outputadaerun.py
Edit4 linesimport yamaaadae = yamaa.yamaa_domain("spec.yaml").outputadaesolutions/adam-adae-text-cleanup/result.R
Edit34 lines# Reference solution for the yamaa benchmark adam-adae-text-cleanup (R track).## Written from the benchmark's full prompt and its inputs alone, as an agent# would write it. Harbor's oracle agent runs it to check that the prompt can# be solved and that the grader scores a correct answer 1.library(dplyr, warn.conflicts = FALSE)library(readr)library(stringr)ae <- read_csv( "/app/input/ae.csv", col_types = cols(AESEQ = col_integer(), .default = col_character()))adae <- ae |> mutate( # AE, a hyphen, and exactly three digits; missing is 0, any other shape # is -1, and the digits give the number. AEREFNUM = case_when( is.na(AESPID) | AESPID == "" ~ 0L, str_detect(AESPID, "^AE-[0-9]{3}$") ~ 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
Edit52 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.from pathlib import Pathimport polars as plae = pl.read_csv("/app/input/ae.csv", infer_schema=False).with_columns( pl.col("AESEQ").cast(pl.Int64))# AE, a hyphen, and exactly three digits; missing is 0, any other shape is# -1, and the digits give the number.aerefnum = ( pl.when(pl.col("AESPID").is_null() | (pl.col("AESPID") == "")) .then(0) .when(pl.col("AESPID").str.contains(r"^AE-[0-9]{3}$")) .then(pl.col("AESPID").str.slice(3).cast(pl.Int64)) .otherwise(-1) .cast(pl.Int64))aerellc = ( pl.when(pl.col("AEREL").is_null() | (pl.col("AEREL") == "")) .then(pl.lit("not reported")) .otherwise(pl.col("AEREL").str.to_lowercase()))adae = ( ae.with_columns( AEREFNUM=aerefnum, AETERMLO=pl.col("AETERM").str.to_lowercase(), AERELLC=aerellc, ) .with_columns(AREL=pl.col("AERELLC").str.to_uppercase()) .select( "STUDYID", "USUBJID", "AESEQ", "AESPID", "AEREFNUM", "AETERM", "AETERMLO", "AERELLC", "AREL", ))Path("/app/output").mkdir(exist_ok=True)adae.write_csv("/app/output/adae.csv")solutions/adam-adae-text-cleanup/result.sas
Edit25 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;
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