Bibliographic record
Abstract
Abstract Li–Fraumeni syndrome is a rare inherited predisposition to the development of multiple cancer phenotypes at an early age which is attributed primarily to germline mutations of the TP53 tumour suppressor gene. p53 plays a major role in the control of cellular growth, cell cycle arrest, DNA repair, apoptosis and senescence, particularly in response to DNA damage and other cellular stressors. The tumours most closely associated with LFS are called ‘core’ cancers and include soft tissue sarcomas, osteosarcoma, premenopausal breast cancer, brain tumours and adrenocortical carcinomas. Identification of these families is important in the study of cancer and the development of ethical and appropriate screening modalities, prevention, early cancer detection and treatment of individuals who harbour alterations of such cancer genes, in order that we may one day improve their prognosis. Key Concepts Li‐Fraumeni syndrome is caused by germline TP53 mutations. p53 is fundamental to DNA damage repair and genome stability. Li‐Fraumeni syndrome is associated with a wide range of early‐onset cancers. Comprehensive surveillance of TP53 mutation carriers is associated with early tumor detection and improved clinical outcomes. There are currently no effective pharmacologic measures to prevent cancer in TP53 mutation carriers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".