Identifying Lynch Syndrome in Patients With Endometrial Carcinoma
Bibliographic record
Abstract
It has been suggested that reflex testing for Lynch syndrome (LS) using mismatch repair immunohistochemistry and/or microsatellite instability analysis in newly diagnosed colorectal carcinoma (CRC) patients is an emerging standard of care in the United States. The risk of gynecologic malignancy in women with LS approaches and even exceeds that of CRC. Furthermore, gynecologic malignancies are often the sentinel cancers in these patients. There is significant variation in practice, but some groups have similarly recommended deployment of reflex testing strategies in patients presenting with endometrial cancer (EC). The College of American Pathologists has stated that pathologists should recognize the histologic and clinical features that should prompt at least a recommendation for mismatch repair testing. Morphologic and clinical schemas in EC to identify microsatellite unstable/LS tumors are less refined than the colon-centric schemas (Amsterdam, Bethesda, and MsPath). Studies of LS EC are few and interpretation is limited by recruitment strategies and the myriad of definitions and study designs used. Although serous cell type is used to triage ovarian cancer patients for BRCA screening, cell type correlation in LS is less certain but seems to involve a spectrum of cell types. We review the morphologic and clinical features/schemas in LS EC and highlight limitations of restrictive aged-based screening strategies, uncertainty in current clinical schemas and equivocal results of morphologic studies of LS EC. With uncertainty of histologic and clinical schemas, and following developments in CRC, reflex testing of all/vast majority of newly diagnosed EC for LS should be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
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".