Use of Mismatch Repair Immunohistochemistry and Microsatellite Instability Testing
Bibliographic record
Abstract
BACKGROUND: The mismatch repair (MMR) status of tumors is being increasingly recognized as a prognostic, predictive, and possible germline predisposition/Lynch syndrome (LS) biomarker in colorectal cancer and other cancer types, particularly in endometrial cancer. Current methods (clinical history and tumor morphology) to predict MMR deficiency (dMMR) are suboptimal, and implementation of reflex laboratory testing of appropriate tumors has been recommended, a strategy requiring test standardization and clinical coordination. METHODS: Two web-based questionnaires were administered, a general and a specialist laboratory questionnaire, to establish the availability of such tests, requisite clinical/pathology integration, current mode of test initiation, reporting and recommendation practices, and education and attitudes among pathologists. Technical aspects were reviewed on the basis of specialist laboratory practice. RESULTS: Of 76 respondents, 21.5% were unaware or were uncertain whether they had access to MMR immunohistochemistry. Although 78.9% of respondents had access to such testing, an integrated approach to the identification of patients with LS is lacking, being limited to just 9 centers. The majority (70%) of testing is clinician initiated, with variable implementation of reflex testing and divergent practices in recommendation to test. Standardized reporting is lacking in many centers. Education on MMR in endometrial cancer is poor compared with that in colorectal cancer (P<0.0001). INTERPRETATION: This multicenter questionnaire highlights heterogenous practices in dMMR testing and LS identification, both in clinical terms and with regard to technical aspects of testing. An integrated multidisciplinary approach is lacking, and there is a need to educate physicians and resolve ethical issues. A Canadian consensus statement and national guidelines on dMMR testing are urgently needed, requiring input from pathologists, clinicians, and genetic counselors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".