Using Family History Forms in Pediatric Oncology to Identify Patients for Genetic Assessment
Bibliographic record
Abstract
Objective: We set out to identify and offer genetic testing to the 5–10% of pediatric cancer patients who have been estimated to carry germline mutations in inherited cancer predisposition syndromes. Clinical genetic testing has become widely available, and thus in busy oncology clinics, tools are needed to identify patients who could benefit from a referral to genetics. Methods: We studied the clinical utility of administering a family history form in the pediatric oncology long-term follow-up clinic to identify patients who might have an inherited cancer predisposition syndrome. Genetic testing involved primarily Sanger sequencing in clia (Clinical Laboratory Improvement Amendments)–certified laboratories. Results: Of 57 patients who completed forms, 19 (33.3%) met criteria for referral to genetics. A significant family history of cancer was present for 4 patients, and 12 patients underwent genetic testing. Of 18 genetic tests ordered, none identified a pathogenic mutation, likely because of a small sample size and a candidate-gene approach to testing. Three families were also identified for further assessment based on a family history of breast cancer, with two of families having members eligible for BRCA1 and BRCA2 testing. Conclusions: Genetic testing in pediatric oncology patients is important to guide the management of patients who have an inherited cancer predisposition syndrome and to identify other family members at risk when mutations are identified. When no mutations are identified, that information is often reassuring to families who are worried about siblings. However, in the absence of an identified genetic cause in a patient, some uncertainty remains.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".