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Summary

Lifecycle: draftEditUnder review
What this benchmark means
README.md

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

Goal: build the change parameters _ALB and _BILI from albumin and bilirubin lab results, carrying each visit's previous result (PREV_AVAL) and the change from it (CHG).

Input: laboratory (LB) records carrying a test code (LBTESTCD), a numeric result (LBSTRESN), and a visit number (VISITNUM). Five subjects: one with both tests across several visits, one with both tests but a single albumin visit, one whose first albumin visit has no result plus a single bilirubin visit, one albumin-only, and one with hemoglobin only, which produces no rows.

Variables:

  • AVISITN: the analysis visit number, taken from the lab visit number.
  • AVAL: the numeric lab result for the visit.
  • PREV_AVAL: the AVAL of the visit just before in AVISITN order, for the same subject and parameter, so an _ALB row never reads a _BILI value; blank on the subject's first visit for the parameter.
  • CHG: AVAL - PREV_AVAL, the change since that visit; blank whenever PREV_AVAL is.

Note: a record with no numeric result gets no row, so the previous value and the change skip that visit and compare with the latest earlier visit that has a result. CHG here is the change from the previous visit, not the ADaMIG "change from baseline" definition. Single-visit subjects have blank PREV_AVAL and CHG; equal consecutive values give CHG 0.

Inputs

Under review
1 source file

input/lb.csv

Edit
19 rows
input/lb.csv
STUDYIDUSUBJIDLBTESTCDLBSTRESNVISITNUM
YAMAA-01ALB4.01
YAMAA-01ALB4.52
YAMAA-01ALB—3
YAMAA-01ALB4.754
YAMAA-01BILI1.01
YAMAA-01BILI1.252
YAMAA-01BILI1.53
YAMAA-01ALB3.751
YAMAA-01BILI2.01
YAMAA-01BILI2.252
YAMAA-01ALB—1
YAMAA-01ALB3.52
YAMAA-01ALB3.753
YAMAA-01BILI0.751
YAMAA-01ALB4.01
YAMAA-01ALB4.02
YAMAA-01ALB3.53
YAMAA-01HGB14.21
YAMAA-01HGB14.52

Expected output

Under review
15 expected rows

expected/adlbc.csv

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15 rows
expected/adlbc.csv
STUDYIDUSUBJIDPARAMCDAVISITNAVALPREV_AVALCHG
YAMAA-01_ALB14——
YAMAA-01_ALB24.540.5
YAMAA-01_ALB44.754.50.25
YAMAA-01_BILI11——
YAMAA-01_BILI21.2510.25
YAMAA-01_BILI31.51.250.25
YAMAA-01_ALB13.75——
YAMAA-01_BILI12——
YAMAA-01_BILI22.2520.25
YAMAA-01_ALB23.5——
YAMAA-01_ALB33.753.50.25
YAMAA-01_BILI10.75——
YAMAA-01_ALB14——
YAMAA-01_ALB2440
YAMAA-01_ALB33.54-0.5
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
ADLBCSTUDYIDStudy IdentifiercharacterCopy value from LB.STUDYID.Predecessor
ADLBCUSUBJIDUnique Subject IdentifiercharacterCopy value from LB.USUBJID.Predecessor
ADLBCPARAMCDParameter CodecharacterSet per constructed row; see the Row construction sheet.Derived
ADLBCAVISITNAnalysis Visit NumbernumericCopy value from LB.VISITNUM.Predecessor
ADLBCAVALAnalysis ValuenumericCopy value from LB.LBSTRESN.Predecessor
ADLBCPREV_AVALPrevious Analysis ValuenumericTake AVAL from 1 row earlier within each group of USUBJID, PARAMCD, ordered by AVISITN.Derived
ADLBCCHGChange from Previous ValuenumericCompute AVAL - PREV_AVAL.Derived
TemplateInclude whenPARAMCD
change_paramsLB.LBTESTCD IN ('ALB', 'BILI') AND LB.LBSTRESN IS NOT NULLRecode LB.LBTESTCD: "ALB" → "_ALB", "BILI" → "_BILI"; missing or unlisted values stay missing
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-adlbc-row-window/full.md

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24 lines

Following CDISC ADaM standards, use the provided LB dataset to create an ADLBC dataset with one record per subject per parameter per visit.

The output dataset should contain the following columns in this order: STUDYID, USUBJID, PARAMCD, AVISITN, AVAL, PREV_AVAL, CHG

  • The dataset holds rows only for LB records with test code ALB or BILI and a numeric result; records with another test code or no numeric result have no row.
  • PARAMCD is "_ALB" for an albumin (ALB) record and "_BILI" for a bilirubin (BILI) record.
  • AVISITN is the lab visit number, and AVAL is the numeric lab result for the visit.
  • PREV_AVAL is the AVAL of the latest earlier visit with a result for the same subject and parameter in AVISITN order, so an "_ALB" row never takes a value from a "_BILI" row.
  • PREV_AVAL has no value for the earliest visit with a result for a subject and parameter.
  • CHG is AVAL - PREV_AVAL, the change since the previous visit rather than a change from baseline; it has no value whenever PREV_AVAL has no value, and equal consecutive values give CHG 0.

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

prompts/adam-adlbc-row-window/conventions.md

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11 lines

Following CDISC ADaM standards, use the provided LB dataset to create an ADLBC dataset with one record per subject per parameter per visit.

The output dataset should contain the following columns in this order: STUDYID, USUBJID, PARAMCD, AVISITN, AVAL, PREV_AVAL, CHG

PARAMCD is "_ALB" for an albumin (ALB) record and "_BILI" for a bilirubin (BILI) record.

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

prompts/adam-adlbc-row-window/brief.md

Edit
8 lines

Following CDISC ADaM standards, use the provided LB dataset to create an ADLBC dataset with one record per subject per parameter per visit.

The output dataset should contain the following columns in this order: STUDYID, USUBJID, PARAMCD, AVISITN, AVAL, PREV_AVAL, CHG

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

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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-adlbc-row-window/result.R

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41 lines
# Reference solution for the yamaa benchmark adam-adlbc-row-window (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)lb <- read_csv(  "/app/input/lb.csv",  col_types = cols(    LBSTRESN = col_double(),    VISITNUM = col_integer(),    .default = col_character()  ))# Build the change parameters from albumin and bilirubin; any other test# produces no rows. A record with no numeric result gets no row.adbc <- lb |>  filter(LBTESTCD %in% c("ALB", "BILI"), !is.na(LBSTRESN), !is.na(VISITNUM)) |>  mutate(    PARAMCD = case_when(      LBTESTCD == "ALB" ~ "_ALB",      LBTESTCD == "BILI" ~ "_BILI"    ),    AVISITN = VISITNUM,    AVAL = LBSTRESN  ) |>  arrange(USUBJID, PARAMCD, AVISITN) |>  group_by(USUBJID, PARAMCD) |>  mutate(    PREV_AVAL = lag(AVAL),    CHG = AVAL - PREV_AVAL  ) |>  ungroup() |>  select(STUDYID, USUBJID, PARAMCD, AVISITN, AVAL, PREV_AVAL, CHG)dir.create("/app/output", showWarnings = FALSE)write_csv(adbc, "/app/output/adlbc.csv", na = "")

solutions/adam-adlbc-row-window/result.py

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95 lines
# Reference solution for the yamaa benchmark adam-adlbc-row-window (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."""Create ADLBC from LB: one record per subject per parameter per visit,with the previous visit's result and the change since the previous visit."""import csvfrom decimal import Decimal, InvalidOperationINPUT_CSV = "/app/input/lb.csv"OUTPUT_CSV = "/app/output/adlbc.csv"PARAM_BY_TEST = {"ALB": "_ALB", "BILI": "_BILI"}def parse_number(text):    """Return the Decimal value of a collected result, or None."""    if text is None:        return None    text = text.strip()    if not text:        return None    try:        return Decimal(text)    except InvalidOperation:        return Nonedef fmt(value):    """Render a Decimal without exponent notation, or blank for None."""    if value is None:        return ""    return format(value, "f")def main():    with open(INPUT_CSV, newline="") as handle:        rows = list(csv.DictReader(handle))    # Keep only ALB/BILI records with a numeric result.    kept = []    for row in rows:        testcd = (row.get("LBTESTCD") or "").strip()        if testcd not in PARAM_BY_TEST:            continue        aval = parse_number(row.get("LBSTRESN"))        if aval is None:            continue        kept.append(            {                "STUDYID": row["STUDYID"],                "USUBJID": row["USUBJID"],                "PARAMCD": PARAM_BY_TEST[testcd],                "AVISITN": int(row["VISITNUM"]),                "AVAL": aval,            }        )    # Order by subject, parameter, then visit number so each row's    # previous value is the latest earlier visit for the same    # subject and parameter.    kept.sort(key=lambda r: (r["STUDYID"], r["USUBJID"], r["PARAMCD"], r["AVISITN"]))    previous = {}    out_rows = []    for record in kept:        key = (record["STUDYID"], record["USUBJID"], record["PARAMCD"])        prev = previous.get(key)        chg = record["AVAL"] - prev if prev is not None else None        out_rows.append(            [                record["STUDYID"],                record["USUBJID"],                record["PARAMCD"],                str(record["AVISITN"]),                fmt(record["AVAL"]),                fmt(prev),                fmt(chg),            ]        )        previous[key] = record["AVAL"]    with open(OUTPUT_CSV, "w", newline="") as handle:        writer = csv.writer(handle)        writer.writerow(            ["STUDYID", "USUBJID", "PARAMCD", "AVISITN", "AVAL", "PREV_AVAL", "CHG"]        )        writer.writerows(out_rows)if __name__ == "__main__":    main()

solutions/adam-adlbc-row-window/result.sas

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28 lines
/* opensas reference solution for the yamaa benchmark adam-adlbc-row-window.   Reads the benchmark inputs and derives the requested output.   Execute with the existing opensas runtime: opensas /app/output/result.sas. */data lb;  length STUDYID USUBJID LBTESTCD $1024;  infile "/app/input/lb.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;  input STUDYID : $char1024. USUBJID : $char1024. LBTESTCD : $char1024. LBSTRESN VISITNUM;run;data collected;  length PARAMCD $8;  set lb; if LBTESTCD in ('ALB','BILI') and not missing(LBSTRESN);  PARAMCD=cats('_',LBTESTCD); AVISITN=VISITNUM; AVAL=LBSTRESN;run;proc sort data=collected; by STUDYID USUBJID PARAMCD AVISITN; run;data result;  retain previous priorchange;  set collected; by STUDYID USUBJID PARAMCD;  if first.PARAMCD then do; previous=.; priorchange=.; end;  PREV_AVAL=previous; CHG=AVAL-PREV_AVAL; PREV2=priorchange;  previous=AVAL; priorchange=CHG;run;proc sql;  create table final as select STUDYID, USUBJID, PARAMCD, AVISITN, AVAL, PREV_AVAL, CHG from result;quit;proc export data=final outfile="/app/output/adlbc.csv" dbms=csv replace; run;