Surveillance recommendations for patients with germline TP53 mutations
Bibliographic record
Abstract
PURPOSE OF REVIEW: Li-Fraumeni syndrome is associated with germline TP53 mutations and carriers have a high lifetime risk of cancer, the most common being sarcoma, breast cancer, brain tumors, adrenocortical carcinoma and leukemia. Germline TP53 mutation carriers are increasingly being identified as more genomic sequencing is performed in both clinical and research settings. There is a pressing clinical need for effective cancer risk management approaches in this group. RECENT FINDINGS: Current clinical surveillance guidelines mainly focus on breast and bowel cancer risk with little consideration for the other cancers common to the syndrome. Imaging technologies are such that the utilization of whole-body MRI imaging for surveillance is viable. Globally, several research groups have included whole-body MRI along with other diagnostic measures in formulating surveillance protocols for TP53 mutation carriers. Early reports suggest a survival benefit. SUMMARY: Surveillance protocols for TP53 mutation carriers have the potential to improve outcomes in individuals and families. Further research is needed to guide the development of an effective and comprehensive surveillance schedule.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".