Differences in histopathological and biochemical outcomes in patients with low Gleason score prostate cancer
Bibliographic record
Abstract
STUDY TYPE: Diagnosis (case series). LEVEL OF EVIDENCE: 4. OBJECTIVE: To test whether the number or percentage of positive biopsy cores can be used to discriminate between patients with prostate cancer of a favourable and less favourable Gleason score (GS) < or =3 + 3, as prognostically, not all GS 3 + 3 prostate cancers are the same. PATIENTS AND METHODS: In all, 1106 consecutive patients with a prostate-specific antigen (PSA) level of < or =10 ng/mL and a biopsy GS of < or =3 + 3 or 3 + 4 had an open radical prostatectomy. The number of positive biopsy cores (< or =2 vs > or =3) were stratified into low- vs high-risk groups. Subsequently, we stratified patients according to the GS and the percentage of positive biopsy cores (<50% vs > or =50%). The pathological stage and the 5-year biochemical recurrence (BCR)-free survival rates were examined in univariable and multivariable models. RESULTS: Based on the number of positive cores, the rate of extraprostatic disease was 11.7% and 23.3%, respectively, in the low-and high-risk GS < or =3 + 3 groups (P < 0.001). The 5-year BCR-free survival rates were 95.0%, 77.8%, 81.2% and 66.5% for, respectively, low- and high-risk GS < or =3 + 3 and for low- and high-risk GS 3 + 4 patients. Univariable and multivariable intergroup BCR rate differences were statistically significant between low- vs high-risk GS 3 + 3 patients (P < 0.001), but not significant between high-risk GS < or =3 + 3 vs low-risk GS 3 + 4 patients (P = 0.6). Comparable results were obtained when comparisons were made according to the percentage of positive biopsy cores. CONCLUSIONS: Our results corroborate the finding that not all patients with a biopsy GS of < or =3 + 3 prostate cancer have low-risk disease. High-risk GS < or =3 + 3 patients have a similar risk profile as more favourable GS 3 + 4 patients. This finding warrants consideration when deciding on treatment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".