Evaluation of Clinical Criteria for the Identification of Lynch Syndrome among Unselected Patients with Endometrial Cancer
Bibliographic record
Abstract
Clinical criteria, primarily young age of cancer onset and family history of signature cancers, have been developed to identify individuals at elevated risk for Lynch syndrome with the goals of early identification and cancer prevention. In 2007, the Society of Gynecologic Oncology (SGO)-codified criteria for women presenting with gynecologic cancers. These criteria have not been validated in a population-based setting. For 412 unselected endometrial cancers, immunohistochemical expression of DNA mismatch repair proteins and MLH1 methylation were assessed to classify tumors as sporadic or probable Lynch syndrome (PLS). In this cohort, 10.5% of patients were designated as PLS based on tumor testing. The sensitivity and specificity of the SGO criteria to identify these same cases were 32.6% [95% confidence interval (CI), 19.2-48.5] and 77% (95% CI, 72.7-81.8), respectively. With the exception of tumor location in the lower uterine segment, multivariate analysis of clinical features, family history, and pathologic variables failed to identify significant differences between the sporadic and PLS groups. A simplified cost-effectiveness analysis demonstrated that the SGO clinical criteria and universal tissue testing strategies had comparable costs per patient with PLS identified. In conclusion, the SGO criteria successfully identify PLS cases among women with endometrial cancer who are young or have significant family history of signature tumors. However, a larger proportion of patients with PLS who are older and have less significant family history are not detected by this screening strategy. Universal tissue testing may be necessary to capture more individuals at risk for having 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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".