Lynch syndrome and cervical cancer
Bibliographic record
Abstract
Carriers of germline mutations in DNA mismatch repair (MMR) genes are at increased risk of several cancers including colorectal and gynecologic cancers (Lynch syndrome). There is no substantial evidence that these mutations are associated with an increased risk of cervical cancer. A total of 369 families with at least one carrier of a mutation in a MMR gene (133 MLH1, 174 MSH2, 35 MSH6 and 27 PMS2) were ascertained via population cancer registries or via family cancer clinics in Australia, New Zealand, Canada, and USA. Personal and family histories of cancer were obtained from participant interviews. Modified segregation analysis was used to estimate the hazard ratio (incidence rates for carriers relative to those for the general population), and age-specific cumulative risks of cervical cancer for carriers. A total of 65 cases of cervical cancer were reported (including 10 verified by pathology reports). The estimated incidence was 5.6 fold (95% CI: 2.3-13.8; p = 0.001) higher for carriers than for the general population with a corresponding cumulative risk to 80 years of 4.5% (95% CI: 1.9-10.7%) compared with 0.8% for the general population. The mean age at diagnosis was 43.1 years (95% CI: 40.0-46.2), 3.9 years younger than the reported USA population mean of 47.0 years (p = 0.02). Women with MMR gene mutations were found to have an increased risk of cervical cancer. Due to limited pathology verification we cannot be certain that a proportion of these cases were not lower uterine segment endometrial cancers involving the endocervix, a recognized cancer of Lynch syndrome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".