Bibliographic record
Abstract
Prevention for paediatric cancers is limited due to the rarity and unpredictability of cancer development in youth. Children are also unique with respect to the types of cancer they develop. The most frequently diagnosed malignancies in young children are leukaemias, tumours of the central nervous system, and lymphomas. This chapter discusses the recognition and screening for cancer predisposition syndrome (CPS), strategies for cancer prevention and the late effects of treatment in these at-risk populations. Poor maternal diet has also been linked to increasing foetal risk of childhood cancer development. Cancer screening presents an opportunity to reduce morbidity and mortality by diagnosing malignancy at an earlier stage. Li-Fraumeni syndrome is the prototypical CPS and is characterized by soft-tissue sarcomas, brain tumours, adrenocortical carcinomas, premenopausal breast cancer, leukaemia, and a myriad of other malignancies presenting at a young age. Familial adenomatous polyposis is a highly penetrant, autosomal-dominant polyposis syndrome caused by mutation of the adenomatous polyposis coli.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".