The impact of the 2005 International Society of Urological Pathology (ISUP) consensus on Gleason grading in contemporary practice
Bibliographic record
Abstract
AIMS: To investigate the impact of the 2005 International Society of Urological Pathology (ISUP) Gleason grading consensus in contemporary practice. METHODS AND RESULTS: The Gleason scores (GS) were compared in two consecutive patient cohorts with matched biopsies and prostatectomies: (i) 908 patients evaluated before the ISUP consensus (July 2000-June 2004) and (ii) 423 patients evaluated after the ISUP consensus (October 2005-June 2007). All biopsies and prostatectomies were performed and scored in one institution and were sampled and processed identically. There was a higher percentage of biopsy and prostatectomy specimens with GS > or = 7 after the ISUP consensus (GS > or = 7 on biopsy in 32% before ISUP versus 46% after ISUP; GS > or = 7 on prostatectomy in 53% before ISUP versus 68% after ISUP; P < 0.001). No significant difference in the complete and + or -1 unit Gleason agreement was found before and after the ISUP consensus. There was a trend towards better complete agreement for GS > or = 7 after the ISUP consensus. CONCLUSIONS: There was a shift towards higher GS on biopsy and prostatectomy in our practice after the ISUP consensus, although - there was no significant impact on the biopsy-prostatectomy Gleason agreement. The significance of this shift for patient management and prognosis is uncertain.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".