Opportunities for Improving Detection of Cancer Predisposition Syndromes in Pediatric Solid Tumor Patients
Bibliographic record
Abstract
BACKGROUND: Detection of cancer predisposition syndromes (CPS) depends on identifying risk factors, including tumor type, family history, and physical findings, to prompt referral for genetic counseling/testing. Whether pediatric oncology providers (POPs) collect adequate family history information is unknown. METHODS: A single-institution retrospective chart review of solid tumor patients <18 years of age referred for a CPS evaluation between January 1, 2017 and January 31, 2019 was performed. POP adherence to American Society of Clinical Oncology (ASCO) family history collection recommendations was measured and compared with genetic counselor performance. Whether sufficient family history was documented to satisfy the criteria of three genetic counseling referral guidelines [American College of Medical Genetics (ACMG), updated Jongmans (UJ), and McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG)] was evaluated. RESULTS: POPs and genetic counselors achieved all 6 ASCO family history metrics in 3% and 99% of 129 eligible cases, respectively. POPs failed to document sufficient family history to satisfy genetic counseling referral criteria in most cases (74% ACMG, 73% UJ, 79% MIPOGG). CONCLUSIONS: POPs perform poorly in family history collection, raising concern that some patients at risk for a CPS based on their family history may not be referred for genetic counseling/testing. Interventions to improve family history collection are needed to enhance CPS detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".