External validation of the novel International Society of Urological Pathology (ISUP) Gleason grading groups in a large contemporary Canadian cohort
Bibliographic record
Abstract
INTRODUCTION: We sought to test the discriminatory ability of the 2014 International Society of Urological Pathology (ISUP) Gleason grading groups (GGG) for predicting biochemical recurrence (BCR) after robot-assisted radical prostatectomy (RARP) in a large, contemporary, Canadian cohort. METHODS: A total of 621 patients who underwent RARP in two major Canadian centres were identified in a prospectively maintained Canadian database between 2006 and 2016. Followup endpoint was BCR. Log-rank test, univariable, and multivariable Cox regression analyses were used. RESULTS: Mean followup was 27.9 months. All five ISUP GGG independently predicted BCR. Statistically significant differences in BCR rates were found between GGG 2 and GGG 3 strata (p<0.001). No statistically significant differences in BCR rates were found between GGG 4 and GGG 5 strata (p=0.3). Relative to GGG 1, the GGG 2, GGG 3, GGG 4, and GGG 5 yielded a 1.10-, 3.44-, 4.18-, and 4.74-fold hazard ratio (HR) increment in BCR, respectively. CONCLUSIONS: This population-based Canadian cohort study confirms the added discriminatory property of the novel ISUP grading, specifically for GGG 2 and GGG 3 strata. No difference, however, was observed between GGG 4 and GGG 5, likely due to the lower number of patients in these groups. As such, after external validation, the 2014 ISUP GGG appears to retain clinical prognostic significance in a Canadian population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".