Uncertainty in the utility of immunohistochemistry in mismatch repair protein expression in epithelial ovarian cancer.
Bibliographic record
Abstract
BACKGROUND: Utility of immunohistochemistry (IHC) for mismatch repair (MMR) protein expression has been demonstrated in colorectal cancer but remains incompletely defined in ovarian cancer. We evaluated MMR protein expression in three population-based samples of epithelial ovarian cancers. MATERIALS AND METHODS: IHC staining was performed on full-section (FS) or tissue microarray (TMA) slides for MLH1, MSH2, and MSH6 expression. RESULTS: Out of 487 cases, 147 and 340 were performed through FS and TMA, respectively. Overall, Loss of Expression (LoE) of at least one MMR protein was observed in 12.7% based on an expression score of ≤3 (on a scale of 9). Notably, LoE was significantly higher in TMAs (17.9%) compared to FS cases (0.7%) (p<0.001). CONCLUSION: A substantial proportion of epithelial ovarian cancers have a loss of MMR protein expression. Protein expression results vary significantly by the tissue sampling methodology utilized, raising concerns about the clinical utility of this test for ovarian tumors.
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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 | 0.085 | 0.071 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".