Accuracy of Cancer Family Histories: Comparison of Two Breast Cancer Syndromes
Bibliographic record
Abstract
Cancer risk programs rely on accurately reported family history information. This study compares the accuracy with which cancer sites and ages at diagnosis are reported by Li-Fraumeni syndrome (LFS) and hereditary breast-ovarian cancer syndrome (HBOCS) families undergoing genetic testing. We analyzed the accuracy of 191 cancer diagnoses among first-degree (FDRs) and second-degree (SDRs) relatives reported by 32 LFS and 52 HBOCS participants in genetic testing programs. Cancer diagnoses of relatives were more accurately reported in the HBOCS cohort (78%) than in the LFS cohort (52%). Almost all breast cancer diagnoses were accurately reported, whereas 74% of ovarian cancer diagnoses and only 55% of other LFS-related cancers were accurately reported. Age at diagnosis was accurate within 5 years for 60% of LFS relatives and 53% of HBOCS relatives. Factors correlating with accurate reporting of cancer history included: being member of BRCA1 family, higher education level, female historian, degree of closeness to affected relative, and having fewer than 5 affected FDRs and SDRs. Relying on verbal histories would not have altered eligibility for genetic testing among HBOCS historians, but fewer than half of LFS historians provided information that would have led to TP53 testing. Our data suggest that it may not be necessary to confirm breast cancer diagnoses routinely; however, documentation of other cancer types remains important for appropriate risk assessment and follow-up.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".