Concordance of “Case Level” Global, Highest, and Largest Volume Cancer Grade Group on Needle Biopsy Versus Grade Group on Radical Prostatectomy
Bibliographic record
Abstract
The practice of assigning "case level" biopsy Grade Group (GG) or Gleason Score is variable. To our knowledge, a comparison of the concordance of different biopsy "case level" GG with the prostatectomy GG has not been done in a post-2005 prostate cancer cohort. We evaluated the GG in 2527 patients who had biopsy and radical prostatectomy performed at our institution between 2005 and 2014. We compared the agreements, the upgrades, and the downgrades of 3 different "case level" biopsy GG, with the final GG: (1) Global GG (sum of most prevalent and highest Gleason grade in any biopsy part/site-specific specimen); (2) Highest GG (found in any biopsy part/site-specific specimen); and (3) Largest Volume Cancer GG (found in any biopsy part/site-specific specimen). The concordance between the biopsy and the final GG were evaluated using weighted kappa (κ) coefficient. The biopsy Global GG, Highest GG, and Largest Volume Cancer GG were the same as the final GG in 60.4%, 57.1%, and 54.3% cases, respectively (weighted κ values: 0.49, 0.48, and 0.44, respectively). When final GG contained tertiary 5, the overall GG agreement decreased: Global GG 41.5%, Highest GG 40.3%, and Largest Volume Cancer GG 37.1% (weighted κ: 0.22, 0.21, and 0.18, respectively). A subset analysis for cases in which the biopsy Global GG and Highest GG were different (n=180) showed an agreement of 62.4% (weighted κ: 0.37) and 18.8% (weighted κ: 0.16), respectively. In patients without a tertiary Gleason pattern on radical prostatectomy, the Global GG and the Highest GG were identical in 92.4% of biopsies. Assigning a biopsy "case level" Global GG versus using the Highest GG and the Largest Volume Cancer GG resulted in comparable and slightly improved agreement with the final GG in this cohort.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".