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.

  1. Task: Would a real study need this derivation, specified this way? Does it follow CDISC SDTM: variable names, controlled terms, record structure? Flag any rule that is ambiguous or that a sponsor would handle differently.
  2. Inputs: Do they look like data collected in a real trial, and do they include the situations you would expect to meet in one?
  3. 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

Lifecycle: draftEditUnder review
What this benchmark means
README.md

Goal: build one record per collected tumor assessment, carrying the collected test, result, unit, method, evaluator, and visit through and adding the standardized numeric result, text result, and standard unit: TRTESTCD, TRTEST, TRORRES, TRORRESU, TRSTRESN, TRSTRESC, TRSTRESU, and TRSTAT.

Input: collected lesion assessments with one row per lesion per visit, carrying the collected result and unit plus study, subject, sequence (TRSEQ), link, method, evaluator, visit, and date identifiers.

Variables:

  • TRLNKID is the link identifier tying the assessment to its lesion in the tumor identification (TU) domain; target, non-target, and new lesions each keep their own identifier.
  • TRTESTCD is the test code as collected: DIAMETER for a measured lesion or TUMSTATE for a lesion state.
  • TRTEST is the test name as collected: Diameter or Tumor State.
  • TRORRES is the result exactly as collected, never overwritten: a diameter, TOO SMALL TO MEASURE, a state such as PRESENT, or empty when the lesion was not assessed.
  • TRORRESU is the unit exactly as collected: mm or cm for a diameter, empty otherwise.
  • TRSTRESC is the standardized character result: a diameter in mm written as text, 5 for a lesion too small to measure, a lesion state kept as collected, and empty when the lesion was not assessed.
  • TRSTRESN is the standardized numeric result in mm: a diameter passes through, a value collected in cm is multiplied by 10, and a lesion too small to measure takes the study convention of 5 mm; it stays empty for lesion states and for lesions not assessed.
  • TRSTRESU is mm whenever a standardized numeric result exists, empty otherwise.
  • TRSTAT is NOT DONE for a lesion not assessed at a visit, empty otherwise.
  • TRMETHOD is the measurement method as collected.
  • TREVAL names the evaluator as collected.
  • VISITNUM is the visit number as collected.
  • TRDTC is the assessment date as collected, empty when the lesion was not assessed.

Note: a lymph-node target lesion contributes its short axis as its diameter, following the study's response criteria; that axis is already the value the sum of diameters needs, so it is carried like any other diameter. A lesion too small to measure is not a zero: it takes the study's conventional 5 mm, and a lesion not assessed stays out of every standardized column rather than counting as zero.

Inputs

Under review
1 source file

input/tr_raw.csv

Edit
10 rows
input/tr_raw.csv
STUDYIDUSUBJIDTRSEQTRLNKIDTRTESTCDTRTESTTRORRESTRORRESUTRSTATTRMETHODTREVALVISITNUMTRDTC
YAMAA-011T01DIAMETERDiameter32mm—CT SCANINVESTIGATOR12026-01-05
YAMAA-012T01DIAMETERDiameter2.8cm—CT SCANINVESTIGATOR22026-03-02
YAMAA-013T01DIAMETERDiameter25mm—CT SCANINVESTIGATOR32026-04-27
YAMAA-014T02DIAMETERDiameter18mm—CT SCANINVESTIGATOR12026-01-05
YAMAA-015T02DIAMETERDiameter——NOT DONECT SCANINVESTIGATOR2—
YAMAA-016T02DIAMETERDiameterTOO SMALL TO MEASUREmm—CT SCANINVESTIGATOR32026-04-27
YAMAA-017NT01TUMSTATETumor StatePRESENT——CT SCANINVESTIGATOR12026-01-05
YAMAA-018NT01TUMSTATETumor StatePRESENT——CT SCANINVESTIGATOR22026-03-02
YAMAA-019NT01TUMSTATETumor StateABSENT——CT SCANINVESTIGATOR32026-04-27
YAMAA-0110NEW01TUMSTATETumor StatePRESENT——CT SCANINVESTIGATOR32026-04-27

Expected output

Under review
10 expected rows

expected/tr.csv

Edit
10 rows
expected/tr.csv
DOMAINSTUDYIDUSUBJIDTRSEQTRLNKIDTRTESTCDTRTESTTRORRESTRORRESUTRSTRESCTRSTRESNTRSTRESUTRSTATTRMETHODTREVALVISITNUMTRDTC
TRYAMAA-011T01DIAMETERDiameter32mm3232mm—CT SCANINVESTIGATOR12026-01-05
TRYAMAA-012T01DIAMETERDiameter2.8cm2828mm—CT SCANINVESTIGATOR22026-03-02
TRYAMAA-013T01DIAMETERDiameter25mm2525mm—CT SCANINVESTIGATOR32026-04-27
TRYAMAA-014T02DIAMETERDiameter18mm1818mm—CT SCANINVESTIGATOR12026-01-05
TRYAMAA-015T02DIAMETERDiameter—————NOT DONECT SCANINVESTIGATOR2—
TRYAMAA-016T02DIAMETERDiameterTOO SMALL TO MEASUREmm55mm—CT SCANINVESTIGATOR32026-04-27
TRYAMAA-017NT01TUMSTATETumor StatePRESENT—PRESENT———CT SCANINVESTIGATOR12026-01-05
TRYAMAA-018NT01TUMSTATETumor StatePRESENT—PRESENT———CT SCANINVESTIGATOR22026-03-02
TRYAMAA-019NT01TUMSTATETumor StateABSENT—ABSENT———CT SCANINVESTIGATOR32026-04-27
TRYAMAA-0110NEW01TUMSTATETumor StatePRESENT—PRESENT———CT SCANINVESTIGATOR32026-04-27
Shaded columns are derived— = missing value

Comments and assessment materials

Comments

Signed in with GitHub; saved in repository Discussions

Mapping spec

Generated from the benchmark YAML; do not edit by hand

Variable NameVariable LabelTypeLengthControlled Terms or FormatOriginCoreConversion DefinitionVariable TypeVariable OrderComments for Define
DOMAINDomain AbbreviationCharAssignedSet constant value "TR".SDTM1
STUDYIDStudy IdentifierCharCollectedCopy value from TR_RAW.STUDYID.SDTM2
USUBJIDUnique Subject IdentifierCharCollectedCopy value from TR_RAW.USUBJID.SDTM3
TRSEQSequence NumberNumCollectedCopy value from TR_RAW.TRSEQ.SDTM4
TRLNKIDLink IDCharCollectedCopy value from TR_RAW.TRLNKID.SDTM5
TRTESTCDTumor Result Test Short NameCharDIAMETER, TUMSTATECollectedCopy value from TR_RAW.TRTESTCD.SDTM6
TRTESTTumor Result Test NameCharCollectedCopy value from TR_RAW.TRTEST.SDTM7
TRORRESResult or Finding in Original UnitsCharCollectedCopy value from TR_RAW.TRORRES.SDTM8
TRORRESUOriginal UnitsCharmm, cmCollectedCopy value from TR_RAW.TRORRESU.SDTM9
TRSTRESNNumeric Result/Finding in Standard UnitsNumDerivedIf TR_RAW.TRORRES = 'TOO SMALL TO MEASURE' then "5"; If TR_RAW.TRTESTCD = 'DIAMETER' AND TR_RAW.TRORRESU = 'cm' then compute TRORRES_N * 10; If TR_RAW.TRTESTCD = 'DIAMETER' then compute TRORRES_N; otherwise blank.SDTM10
TRSTRESCCharacter Result/Finding in Std FormatCharDerivedIf TR_RAW.TRTESTCD = 'DIAMETER' then "TRSTRESN"; otherwise "TR_RAW.TRORRES".SDTM11
TRSTRESUStandard UnitsCharmmDerivedIf TRSTRESN IS NOT NULL then "mm"; otherwise blank.SDTM12
TRSTATCompletion StatusCharNOT DONECollectedCopy value from TR_RAW.TRSTAT.SDTM13
TRMETHODMethod of Test or ExaminationCharCollectedCopy value from TR_RAW.TRMETHOD.SDTM14
TREVALEvaluatorCharCollectedCopy value from TR_RAW.TREVAL.SDTM15
VISITNUMVisit NumberNumCollectedCopy value from TR_RAW.VISITNUM.SDTM16
TRDTCDate/Time of Tumor MeasurementCharISO 8601CollectedCopy value from TR_RAW.TRDTC.SDTM17
VariablePermitted values
TRTESTCDDIAMETER, TUMSTATE
TRORRESUmm, cm
TRSTRESUmm
TRSTATNOT DONE
VersionDateAuthorDescriptionReviewerSign-off
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/sdtm-tr-tumor-measurements/full.md

Edit
34 lines

Following CDISC SDTM standards, use the provided TR_RAW dataset to create a TR dataset with one record per collected tumor assessment.

The output dataset should contain the following columns in this order: DOMAIN, STUDYID, USUBJID, TRSEQ, TRLNKID, TRTESTCD, TRTEST, TRORRES, TRORRESU, TRSTRESC, TRSTRESN, TRSTRESU, TRSTAT, TRMETHOD, TREVAL, VISITNUM, TRDTC

TRLNKID is the link identifier tying the assessment to its lesion in the tumor identification domain. TRTESTCD is DIAMETER for a measured lesion or TUMSTATE for a lesion state; TRTEST is Diameter or Tumor State. TRORRES is the result exactly as collected, never overwritten: a diameter, TOO SMALL TO MEASURE, a state such as PRESENT, or has no value when the lesion was not assessed. TRORRESU is the unit exactly as collected (mm or cm for a diameter), and has no value otherwise.

TRSTRESC is the diameter in mm written as text (a whole number has no decimal point), 5 for a lesion too small to measure, or a lesion state kept as collected; it has no value when the lesion was not assessed. TRSTRESN is the diameter in mm as a number (a value collected in cm is multiplied by 10), or 5 for a lesion too small to measure; it has no value for lesion states and for lesions not assessed. TRSTRESU is mm whenever a standardized numeric result exists, and has no value otherwise. TRSTAT is NOT DONE for a lesion not assessed at a visit, and has no value otherwise. TRMETHOD, TREVAL, and VISITNUM are as collected; TRDTC is the assessment date as collected, and has no value when the lesion was not assessed.

A lymph-node target lesion contributes its short axis as its diameter. A lesion too small to measure is not a zero: it takes the study convention of 5 mm. A lesion not assessed stays out of every standardized column rather than counting as zero.

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

prompts/sdtm-tr-tumor-measurements/conventions.md

Edit
17 lines

Following CDISC SDTM standards, use the provided TR_RAW dataset to create a TR dataset with one record per collected tumor assessment.

The output dataset should contain the following columns in this order: DOMAIN, STUDYID, USUBJID, TRSEQ, TRLNKID, TRTESTCD, TRTEST, TRORRES, TRORRESU, TRSTRESC, TRSTRESN, TRSTRESU, TRSTAT, TRMETHOD, TREVAL, VISITNUM, TRDTC

TRTESTCD is DIAMETER for a measured lesion or TUMSTATE for a lesion state; TRTEST is Diameter or Tumor State. TRORRES is a diameter, TOO SMALL TO MEASURE, a state such as PRESENT, or has no value.

In TRSTRESC a whole number has no decimal point. TRSTRESC and TRSTRESN are 5 for a lesion too small to measure. TRSTAT is NOT DONE or has no value.

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

prompts/sdtm-tr-tumor-measurements/brief.md

Edit
10 lines

Following CDISC SDTM standards, use the provided TR_RAW dataset to create a TR dataset with one record per collected tumor assessment.

The output dataset should contain the following columns in this order: DOMAIN, STUDYID, USUBJID, TRSEQ, TRLNKID, TRTESTCD, TRTEST, TRORRES, TRORRESU, TRSTRESC, TRSTRESN, TRSTRESU, TRSTAT, TRMETHOD, TREVAL, VISITNUM, TRDTC

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

yamaa code

Runs the yamaa specification; its result must match the expected output

run.R

Edit
4 lines
library(yamaa)tr <- yamaa_domain("spec.yaml")$outputtr

run.py

Edit
4 lines
import yamaatr = yamaa.yamaa_domain("spec.yaml").outputtr

Solution without yamaa

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

solutions/sdtm-tr-tumor-measurements/result.R

Edit
61 lines
# Reference solution for the yamaa benchmark sdtm-tr-tumor-measurements (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)fmt_num <- function(x) {  if (is.na(x)) {    return(NA_character_)  }  if (x == floor(x)) {    formatC(x, format = "f", digits = 0)  } else {    formatted <- format(x, scientific = FALSE, trim = TRUE, digits = 15)    sub("\\.0+$", "", formatted)  }}raw <- read_csv(  "/app/input/tr_raw.csv",  col_types = cols(    TRSEQ = col_integer(),    VISITNUM = col_integer(),    .default = col_character()  ))tr <- raw |>  mutate(    orres_num = suppressWarnings(as.numeric(TRORRES)),    mm_value = if_else(TRORRESU == "cm", orres_num * 10, orres_num),    TRSTRESN = case_when(      TRTESTCD == "DIAMETER" & TRORRES == "TOO SMALL TO MEASURE" ~ 5,      TRTESTCD == "DIAMETER" & !is.na(TRORRES) & TRORRES != "" &        TRORRES != "TOO SMALL TO MEASURE" ~ mm_value,      .default = NA_real_    ),    TRSTRESC = case_when(      TRTESTCD == "DIAMETER" & TRORRES == "TOO SMALL TO MEASURE" ~ "5",      TRTESTCD == "DIAMETER" & !is.na(TRORRES) & TRORRES != "" &        TRORRES != "TOO SMALL TO MEASURE" ~ vapply(        TRSTRESN, fmt_num, character(1)      ),      TRTESTCD != "DIAMETER" & !is.na(TRORRES) & TRORRES != "" ~ TRORRES,      .default = NA_character_    ),    DOMAIN = "TR",    TRSTRESU = if_else(!is.na(TRSTRESN), "mm", NA_character_)  ) |>  arrange(USUBJID, TRSEQ) |>  select(    DOMAIN, STUDYID, USUBJID, TRSEQ, TRLNKID, TRTESTCD, TRTEST, TRORRES,    TRORRESU, TRSTRESC, TRSTRESN, TRSTRESU, TRSTAT, TRMETHOD, TREVAL,    VISITNUM, TRDTC  )dir.create("/app/output", showWarnings = FALSE)write_csv(tr, "/app/output/tr.csv", na = "")

solutions/sdtm-tr-tumor-measurements/result.py

Edit
103 lines
# Reference solution for the yamaa benchmark sdtm-tr-tumor-measurements (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 pldef fmt_num(x: float | None) -> str | None:    import math    if x is None:        return None    if isinstance(x, float) and math.isnan(x):        return None    if float(x).is_integer():        return str(int(float(x)))    text = repr(float(x))    if "e" in text or "E" in text:        text = f"{float(x):.15f}".rstrip("0").rstrip(".")    else:        text = text.rstrip("0").rstrip(".") if "." in text else text    return textraw = pl.read_csv("/app/input/tr_raw.csv", infer_schema=False).with_columns(    pl.col("TRSEQ").cast(pl.Int64),    pl.col("VISITNUM").cast(pl.Int64),)is_diameter = pl.col("TRTESTCD") == "DIAMETER"is_too_small = pl.col("TRORRES") == "TOO SMALL TO MEASURE"is_missing = pl.col("TRORRES").is_null() | (pl.col("TRORRES") == "")orres_num = pl.col("TRORRES").cast(pl.Float64, strict=False)mm_value = (    pl.when(pl.col("TRORRESU") == "cm")    .then(orres_num * 10)    .otherwise(orres_num))stresn = (    pl.when(is_diameter & is_too_small)    .then(pl.lit(5.0))    .when(is_diameter & ~is_missing & ~is_too_small)    .then(mm_value)    .otherwise(pl.lit(None, dtype=pl.Float64)))tr = raw.with_columns(TRSTRESN=stresn)# Standardized text: 5 for too small, the lesion state as collected, the# diameter in mm without a decimal point for whole numbers, else no value.rows = tr.to_dicts()for row in rows:    if row["TRTESTCD"] == "DIAMETER":        if row["TRORRES"] == "TOO SMALL TO MEASURE":            row["TRSTRESC"] = "5"        elif row["TRORRES"] is None or row["TRORRES"] == "":            row["TRSTRESC"] = None        else:            row["TRSTRESC"] = fmt_num(row["TRSTRESN"])    else:        if row["TRORRES"] is None or row["TRORRES"] == "":            row["TRSTRESC"] = None        else:            row["TRSTRESC"] = row["TRORRES"]tr = (    pl.DataFrame(rows)    .with_columns(        DOMAIN=pl.lit("TR"),        TRSTRESU=pl.when(pl.col("TRSTRESN").is_not_null())        .then(pl.lit("mm"))        .otherwise(pl.lit(None, dtype=pl.String)),    )    .select(        "DOMAIN",        "STUDYID",        "USUBJID",        "TRSEQ",        "TRLNKID",        "TRTESTCD",        "TRTEST",        "TRORRES",        "TRORRESU",        "TRSTRESC",        "TRSTRESN",        "TRSTRESU",        "TRSTAT",        "TRMETHOD",        "TREVAL",        "VISITNUM",        "TRDTC",    )    .sort(["USUBJID", "TRSEQ"]))Path("/app/output").mkdir(exist_ok=True)tr.write_csv("/app/output/tr.csv")

solutions/sdtm-tr-tumor-measurements/result.sas

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19 lines
/* opensas reference solution for the yamaa benchmark sdtm-tr-tumor-measurements.   Reads the benchmark inputs and derives the requested output.   Execute with the existing opensas runtime: opensas /app/output/result.sas. */data tr_raw;  length STUDYID USUBJID TRLNKID TRTESTCD TRTEST TRORRES TRORRESU TRSTAT TRMETHOD TREVAL TRDTC $1024;  infile "/app/input/tr_raw.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;  input STUDYID : $char1024. USUBJID : $char1024. TRSEQ TRLNKID : $char1024. TRTESTCD : $char1024. TRTEST : $char1024. TRORRES : $char1024. TRORRESU : $char1024. TRSTAT : $char1024. TRMETHOD : $char1024. TREVAL : $char1024. VISITNUM TRDTC : $char1024.;run;data result; set tr_raw; length DOMAIN $2 TRSTRESC TRSTRESU $1024; DOMAIN='TR'; TRSTRESN=.; TRSTRESC=''; TRSTRESU='';if TRSTAT='NOT DONE' or missing(TRORRES) then do; TRSTAT='NOT DONE'; TRORRES=''; TRORRESU=''; TRDTC=''; end;else if TRTESTCD='TUMSTATE' then TRSTRESC=TRORRES;else do; if TRORRES='TOO SMALL TO MEASURE' then TRSTRESN=5; else do; TRSTRESN=input(TRORRES,?? best32.); if TRORRESU='cm' then TRSTRESN=TRSTRESN*10; end;if not missing(TRSTRESN) then do; TRSTRESC=strip(put(TRSTRESN,best32.)); TRSTRESU='mm'; end; end; run;proc sql;  create table final as select DOMAIN, STUDYID, USUBJID, TRSEQ, TRLNKID, TRTESTCD, TRTEST, TRORRES, TRORRESU, TRSTRESC, TRSTRESN, TRSTRESU, TRSTAT, TRMETHOD, TREVAL, VISITNUM, TRDTC from result;quit;proc export data=final outfile="/app/output/tr.csv" dbms=csv replace; run;