Standardization of Gleason grading among 337 European pathologists
Bibliographic record
Abstract
AIMS: The 2005 International Society of Urological Pathology (ISUP) modification of Gleason grading recommended that the highest grade should always be included in the Gleason score (GS) in prostate biopsies. We analysed the impact of this recommendation on reporting of GS 6 versus 7. METHODS AND RESULTS: Fifteen expert uropathologists reached two-thirds consensus on 15 prostate biopsies with GS 6-7 cancer. Eighty-five microphotographs were graded by 337 of 617 members of the European Network of Uropathology (ENUP), representing 19 countries. There was agreement between expert and majority member GS in 12 of 15 cases, while members upgraded in three cases. Among members and the expert consensus, a GS >6 was assigned by 64.5% and 60%, respectively. Mean member GS was higher than consensus GS in nine of 15 cases. A Gleason pattern (GP) 5 was reported by 0.3-5.6% in 10 cases. Agreement between consensus and member GS was 58.2-89.3% (mean 71.4%) in GS 6 cases and 46.3-63.8% (mean 56.4%) in GS 7 cases (P = 0.009). CONCLUSIONS: While undergrading of prostate cancer used to be prevalent, some now tend to overgrade. Minimum diagnostic criteria for GP 4 and 5 in biopsies need to be better defined. Image libraries reviewed by experts may be useful for standardization.
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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 | 0.032 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".