Borderline Gleason scores: communication is the key
Bibliographic record
Abstract
The biopsy Gleason score (GS) is a critical component of patient management having been demonstrated to be an excellent predictor of patient outcome.1 2 While the application of Gleason grading is generally straightforward, grading is subject to significant interobserver variation3 and divergent opinions may confuse clinicians and patients. Interobserver variation may be due to the application of different grading rules or more commonly different interpretations of borderline morphological appearances. The former is avoidable and multiple consensus conferences have sought to define uniform criteria for grading prostate cancer.4–7 However, the latter is inevitable in a morphological continuum. We seek to explain why precise grading becomes less important in this scenario, if the findings are effectively communicated by the pathologist and correctly interpreted by the clinician. Gleason grades commonly represent a morphological continuum from well-formed glands (pattern 3) to increasingly smaller-sized and poorly formed glandular proliferations (pattern 4) and finally to almost no glandular differentiation (pattern 5). Thus, GS is often a continuous variable with arbitrary cut-offs. This is analogous to serum Prostate Specific Antigen (PSA) where arbitrary cut-offs are used to categorise patients into risk groups. However, unlike serum PSA, grade is reported as …
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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 | 0.011 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.027 |
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".