Maximizing cancer prevention through genetic navigation for Lynch syndrome detection in women with newly diagnosed endometrial and nonserous/nonmucinous epithelial ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Despite recommendations for reflex immunohistochemistry (IHC) for mismatch repair (MMR) proteins to identify Lynch syndrome (LS), the uptake of genetic assessment by those who meet referral criteria is low. The authors implemented a comprehensive genetic navigation program to increase the uptake of genetic testing for LS in patients with endometrial cancer (EC) or nonserous/nonmucinous ovarian cancer (OC). METHODS: Participants with newly diagnosed EC or OC were prospectively recruited from 3 cancer centers in Ontario, Canada. Family history questionnaires were used to assess LS-specific family history. Reflex IHC for MMR proteins was performed with the inclusion of clinical directives in pathology reports. A trained genetic navigator initiated a genetic referral on behalf of the treating physician and facilitated genetic referrals to the closest genetics center. RESULTS: A total of 841 participants (642 with EC, 172 with OC, and 27 with synchronous EC/OC) consented to the study; 194 (23%) were MMR-deficient by IHC. Overall, 170 women (20%) were eligible for a genetic assessment for LS: 35 on the basis of their family history alone, 24 on the basis of their family history and IHC, 82 on the basis of IHC alone, and 29 on the basis of clinical discretion. After adjustments for participants who died (n = 6), 149 of 164 patients (91%) completed a genetic assessment, and 111 were offered and completed genetic testing. Thirty-four women (4.0% of the total cohort and 30.6% of those with genetic testing) were diagnosed with LS: 5 with mutL homolog 1 (MLH1), 9 with mutS homolog 2 (MSH2), 15 with mutS homolog 6 (MSH6), and 5 with PMS2. CONCLUSIONS: The introduction of a navigated genetic program resulted in a high rate of genetic assessment (>90%) in patients with gynecologic cancer at risk for LS.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".