Cancer Screening Recommendations for Individuals with Li-Fraumeni Syndrome
Bibliographic record
Abstract
tumor suppressor gene encoding p53, a transcription factor triggered as a protective cellular mechanism against different stressors. Loss of p53 function renders affected individuals highly susceptible to a broad range of solid and hematologic cancers. It has recently become evident that children and adults with LFS benefit from intensive surveillance aimed at early tumor detection. In October 2016, the American Association for Cancer Research held a meeting of international LFS experts to evaluate the current knowledge on LFS and propose consensus surveillance recommendations. Herein, we briefly summarize clinical and genetic aspects of this aggressive cancer predisposition syndrome. In addition, the expert panel concludes that there are sufficient existing data to recommend that all patients with LFS be offered cancer surveillance as soon as the clinical or molecular LFS diagnosis is established. Specifically, the panel recommends adoption of a modified version of the "Toronto protocol" that includes a combination of physical exams, blood tests, and imaging. The panel also recommends that further research be promoted to explore the feasibility and effectiveness of these risk-adapted surveillance and cancer prevention strategies while addressing the psychosocial needs of individuals and families with LFS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".