Identifying Lynch Syndrome in Patients With Ovarian Carcinoma
Bibliographic record
Abstract
Up to 15% of ovarian cancers are etiologically linked with hereditary susceptibility. Within this group, germline mutations in mismatch repair (MMR) genes, known otherwise as Lynch syndrome (LS), account for the majority of cases that are not associated with mutations in BRCA1 or BRCA2. Clinical schemas specific for gynecologic cancers have been developed to identify patients with LS; however, many of the recommendations are poorly defined. Few case series of germline-confirmed LS-associated ovarian cancers have been reported, limited by small sample size and often lacking central pathology review. Much insight has been gained from studies of unselected cohorts, using immunohistochemical assessment of MMR protein expression or microsatellite instability analysis. In spite of contradictory results, likely reflective of differences in study design, sample size and methodology, a recurring observation is the overrepresentation of "endometriosis-associated tumors," namely, endometrioid and clear cell subtypes, in the group of ovarian tumors with MMR deficiency. In this review, we summarize the clinical and histomorphologic features of LS-associated/MMR-deficient ovarian epithelial cancers and recommend that reflex testing be performed on the basis of tumor subtype.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".