Pediatric Cancer Predisposition and Surveillance: An Overview, and a Tribute to Alfred G. Knudson Jr
Bibliographic record
Abstract
The prevalence of childhood cancer attributable to genetic predisposition was generally considered very low. However, recent reports suggest that at least 10% of pediatric cancer patients harbor a germline mutation in a cancer predisposition gene. Although some of these children will have a family history suggestive of a cancer predisposition syndrome, many others will not. Evidence from recent pediatric studies suggests that surveillance and early detection of cancer in individuals carrying a germline cancer predisposing mutation may result in improved outcomes. However, there is a lack of consistency in the design of cancer surveillance regimens across centers both nationally and internationally. To standardize approaches, the Pediatric Cancer Working Group of the American Association for Cancer Research (AACR) convened a workshop, during which consensus screening recommendations for children with the most common cancer predisposition syndromes were developed. In general, we considered a 5% or greater chance of developing a childhood cancer to be a reasonable threshold to recommend screening. Conditions for which the cancer risk was between 1% to 5% were addressed individually. In a series of manuscripts accompanying this article, we provide recommendations for surveillance, focusing on when to initiate and/or discontinue specific screening measures, which modalities to use, and the frequency of screening. Points of controversy are also reviewed. We present the outcome of our deliberations on consensus screening recommendations for specific disorders in 18 position articles as Open Access publications, which are freely available on an AACR-managed website.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".