RESEARCH Decisions about prophylac n
Bibliographic record
Abstract
li disposing carriers to a high risk of early-onset colorectal cancer [1-5]. LS is caused by mutations in four mis-gynecologic cancers have two risk management options: 1) increased cancer surveillance, or 2) surgical removal Etchegary et al. Hereditary Cancer in Clinical Practice (2015) 13:10 DOI 10.1186/s13053-015-0031-4Lynch mutation carriers is not supported by researchSt. John’s A1B 3V6, NL, Canada Full list of author information is available at the end of the articlematch repair genes: MLH1, MSH2, MSH6, and PMS2 [1]. In addition to their increased colon cancer risk, women carrying these germline mutations have dramat-ically elevated rates of gynecological cancer compared to women in the general population. They face a 40-60% of the uterus (hysterectomy) and/or removal of the ovar-ies and the fallopian tubes known as risk-reducing salpingo-oophorectomy (RRSO). Since some Lynch-associated cancers are diagnosed before the age of 35, some authors recommend annual surveillance in this high-risk group of mutation carriers, including transva-ginal ultrasonography, tumor marker CA125 blood tests and/or endometrial biopsy [6,7]. At present, however, the benefit of screening for gynecological cancers in
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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 | 0.058 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.018 |
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".