Diagnosis of Ovarian Carcinoma Cell Type is Highly Reproducible
Bibliographic record
Abstract
Reproducible diagnosis of ovarian carcinoma cell types is critical for cell type-specific treatment. The purpose of this study was to test the reproducibility of cell type diagnosis across Canada. Analysis of the interobserver reproducibility of histologic tumor type was performed among 6 pathologists after brief training in the use of modified World Health Organization criteria to classify ovarian carcinomas into 1 of 6 categories: high-grade serous, endometrioid, clear cell, mucinous, low-grade serous, and other. These 6 pathologists independently reviewed a test set of 40 ovarian carcinomas. A validation set of 88 consecutive ovarian carcinomas drawn from 5 centers was subject to local review by 1 of the 6 study pathologists, and central review by a single observer. Interobserver agreement was assessed through calculation of concordance and kappa values for pair-wise comparison. For the test set, the paired concordance between pathologists in cell type diagnosis ranged from 85.0% to 97.5% (average 92.3%), and the kappa values were 0.80 to 0.97 (average 0.89). Inclusion of immunostaining results did not significantly improve reproducibility (P=0.69). For the validation set, the concordance between original diagnosis and local review was 84% and between local review and central review was 94%. The kappa values were 0.73 and 0.89, respectively. With a brief training exercise and the use of defined criteria for ovarian carcinoma subtyping, there is excellent interobserver reproducibility in diagnosis of cell type. This has implications for clinical trials of subtype-specific ovarian carcinoma treatments.
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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 | 0.031 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".