Pediatric Cancer Predisposition and Surveillance Update: Summary Perspective and Future Directions
Bibliographic record
Abstract
An increasing number of studies suggest that a significant proportion of children with cancer harbor an underlying predisposition to malignancy, and it is likely that this proportion will only increase. Targeted surveillance for these individuals would likely improve outcomes. Historically, however, for most predisposition syndromes, there were no standardized surveillance protocols for early detection of cancer in predisposed individuals. Therefore, the Pediatric Cancer Working Group of the American Association for Cancer Research convened a workshop in 2016 to develop consensus surveillance recommendations (published in 2017) for children and adolescents with the most common cancer predisposition syndromes. These recommendations provided a consistent approach for pediatric oncologists and other care providers to use as a plan for cancer surveillance in pediatric patients with these syndromes. We held a second workshop in 2023 to update recommendations based upon new data, as well as to add syndromes that were newly described or not addressed in the prior workshop. The resulting articles represent updated surveillance recommendations for currently recognized predisposition syndromes, organized along similar themes. We also address novel approaches to surveillance that are under investigation, as well as prospects for prevention trials for these high-risk populations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".