Mismatch repair (MMR) assay uptake and use in early colorectal cancer patients with MMR-deficient and MMR-proficient tumors
Bibliographic record
Abstract
Three biomarkers for immune checkpoint inhibitors-mismatch repair (MMR)/microsatellite instability, tumor mutation burden, and PD-L1 ligand expression-are currently approved for utilization in therapeutic decisions. The adoption of these biomarkers has gained ground gradually following demonstrated clinical utility. The colorectal cancer database of our cancer program was reviewed, and data on the availability and on the results of MMR molecular evaluation of patients diagnosed with stage 1-3 colorectal cancer over 8 years were extracted from medical records. MMR evaluation was available in a higher percentage of patients diagnosed with non-metastatic colorectal cancer in most recent years. MMR was performed more often in patients with more advanced stage (stage 2 and 3) and in patients with right colon locations. MMR deficiency was associated with right colon cancers, high grade, and thrombocytosis. The prevalence of MMR deficiency was 23.1%, and the main defects leading to MMR deficiency were losses of MLH1 and PMS2 nuclear staining, which were present in 86.7% of patients with MMR deficiency. MMR testing showed an increasing up-take over the years, with a significant increase of performance since the approval of immunotherapy in metastatic patients with microsatellite instability/MMR deficiency.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".