The Future of Surveillance in the Context of Cancer Predisposition: Through the Murky Looking Glass
Bibliographic record
Abstract
Abstract At least 10% of children with cancer harbor a disease-associated pathogenic variant in a known cancer predisposition gene. It is widely accepted that pathogenic variants affecting other genes, epigenetic factors, or abnormalities in additional gene products may contribute to the etiology of many more childhood cancers. Effective preventive measures exist for only a few cancer types associated with predisposing conditions, but the development and implementation of surveillance protocols aimed at reducing morbidity and mortality in at-risk children through the early detection of cancer has emerged as an important clinical tool. The articles in this Clinical Cancer Research series present international consensus generated recommendations for surveillance for a wide spectrum of cancer predisposition syndromes affecting children. In this article, we explore the challenges and opportunities for researchers and practitioners in the many fields affiliated with pediatric cancer, and we offer insights into what the future might hold as we continue our efforts to mitigate the impact of cancer susceptibility on children, their families and society. Clin Cancer Res; 23(21); e133–e7. ©2017 AACR. See all articles in the online-only CCR Pediatric Oncology Series.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".