Abstract IA04: New approaches to study cancer predisposition syndromes
Bibliographic record
Abstract
Abstract The diagnosis of a cancer predisposition syndrome (CPS) in a child diagnosed with cancer influences medical care; however, many open questions remain in the areas of counseling, psychosocial support, cancer prevention, surveillance, and therapy. To address such issues, a CPS-registry was launched: www.cancer-predisposition.org. The CPS-registry (1) provides detailed information for affected families and health care professionals, (2) collects biospecimens for translational research, (3) assembles both retrospective and annual prospective data on cancer diagnoses and surveillance measures in individuals with approximately 60 different CPS, (4) makes mutation data publicly available through accessible databases, (5) shares data internationally, (6) links data with trial groups and cancer registries, and (7) provides expert opinion for individual treatment decisions. Due to hidden clinical signs, CPS are often overlooked; however, various clinical tools have been developed to increase the percentage of families that are being offered counseling and testing. Moreover, CPS diagnosis is dramatically facilitated by the increasing use of agnostic next-generation sequencing in pediatric oncology, supporting the notion that 10% of children with cancer harbor mutations in cancer predisposition genes, often in the absence of clinical signs. CPS diagnosis yet remains a challenge prior to a first cancer diagnosis; however, a more systematic diagnostic approach is required to offer prevention and surveillance measures to these individuals. Strong family support organizations such as LFSA and FARF have dramatically improved collaborative research in the CPS field. In the future, clinical protocols for CPS individuals will pave the way for better prevention, surveillance, and cancer treatment strategies. Citation Format: Christian P. Kratz. New approaches to study cancer predisposition syndromes [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA04.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".