Interobserver Agreement for Endometrial Cancer Characteristics Evaluated on Biopsy Material
Bibliographic record
Abstract
A shift toward a disease-based therapy designed according to patterns of failure and likelihood of nodal involvement predicted by pathologic determinants has recently led to considering a selective approach to lymphadenectomy for endometrial cancer. Therefore, it became critical to examine reproducibility of diagnosing the key determinants of risk, on preoperative endometrial tissue samples as well as the concordance between preoperative and postresection specimens. Six gynaecologic pathologists assessed 105 consecutive endometrial biopsies originally reported as positive for endometrial cancer for cell type (endometrioid versus nonendometrioid), tumor grade (FIGO 3-tiered and 2-tiered), nuclear grade, and risk category (low risk defined as endometrioid histology, grade 1 + 2 and nuclear grade <3). Interrater agreement levels were substantial for identification of nonendometrioid histology (κ = 0.63; SE = 0.025), high tumor grade (κ = 0.64; SE = 0.025), and risk category (κ = 0.66; SE = 0.025). The overall agreement was fair for nuclear grade (κ = 0.21; SE = 0.025). There is agreement amongst pathologists in identifying high-risk pathologic determinants on endometrial cancer biopsies, and these highly correlate with postresection specimens. This is ascertainment prerequisite adaptation of the paradigm shift in surgical staging of patients with endometrial cancer.
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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 | 0.052 | 0.110 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".