Frequency and face validity of reported family history of cancer in first‐degree relatives and genetic syndromes among children with cancer in Project:EveryChild: A report from the Children’s Oncology Group
Bibliographic record
Abstract
BACKGROUND: Taking a family history of cancer (FH) is essential for identifying individuals with heritable cancer predisposition. PROJECT: EveryChild (Children's Oncology Group [COG] trial APEC14B1), the registration and biobanking protocol of the COG, includes suggested questions about FH in first-degree relatives and personal history of genetic syndromes (GS) in pediatric oncology patients. The validity of these items is unclear; therefore, the authors assessed the data quality and face validity of the responses. METHODS: The authors analyzed case report forms regarding FH and GS of 30,157 participants (aged birth to 21 years) with newly diagnosed pediatric cancer enrolled in APEC14B1. FH and GS data were manually curated to interpret the responses and group them into categories, followed by face validity assessment-defined as the extent to which the information provided represented what it was intended to capture. RESULTS: Responses were provided for 65.7% of participants (n = 19,810), with 6.1% reporting FH (n = 1204). Of those, 97.9% (n = 1178) included sufficient free-text detail to assess face validity, although 49.4% required manual interpretation. Among FH reports, 48.3% (n = 595) were suggestive of heritable cancer risk. GS was reported in 4.3% of responders (n = 863), with 93.3% (n = 780) showing face validity after curation. Down syndrome (n = 302) and neurofibromatosis type 1 (n = 93) were the most frequently reported syndromes, with neurofibromatosis type 1 most common in patients who had central nervous system tumors. CONCLUSIONS: Despite limitations and the need for manual curation, FH and GS data collected by using proposed questions were sufficient to identify known heritable cancer patterns. These findings support questionnaire-based data collection and highlight areas for improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".