Testing Women With Endometrial Cancer to Detect Lynch Syndrome
Bibliographic record
Abstract
PURPOSE: Women with endometrial cancer as a result of Lynch syndrome may not be identified as such by Amsterdam II criteria. We estimated the costs and benefits of different testing criteria to identify Lynch syndrome in women with endometrial cancer. METHODS: We developed a Markov Monte Carlo simulation model to compare six criteria for Lynch syndrome testing for women with endometrial cancer: Amsterdam II criteria; age younger than 50 years with at least one first-degree relative having a Lynch-associated cancer at any age (FDR); immunohistochemistry (IHC) triage if age younger than 50 years; IHC triage if age younger than 60 years; IHC triage at any age if 1 FDR; and IHC triage of all endometrial cancers. Net health benefit was life expectancy, and primary outcome was the incremental cost-effectiveness ratio (ICER). The model estimated the number of new colorectal cancers associated with each strategy. RESULTS: IHC triage of women with endometrial cancer having at least 1 FDR yielded a favorable ICER of $9,126 per year of life gained. This strategy would subject fewer cases to IHC but identify more mutation carriers than age thresholds of 50 or 60 years. IHC triage of all endometrial cancers could identify the most mutation carriers and prevent the most colorectal cancers but at considerable cost ($648,494 per year of life gained). CONCLUSION: IHC triage of women with endometrial cancer at any age having at least 1 FDR with a Lynch-associated cancer is a cost-effective strategy for detecting 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".