Non‐organ confined stage and upgrading rates in exclusive PSA high‐risk prostate cancer patients
Bibliographic record
Abstract
BACKGROUND: The pathological stage of prostate cancer with high-risk prostate-specific antigen (PSA) levels, but otherwise favorable and/or intermediate risk characteristics (clinical T-stage, Gleason Grade group at biopsy [B-GGG]) is unknown. We hypothesized that a considerable proportion of such patients will exhibit clinically meaningful GGG upgrading or non-organ confined (NOC) stage at radical prostatectomy (RP). MATERIALS AND METHODS: Within the Surveillance, Epidemiology, and End Results database (2010-2015) we identified RP-patients with cT1c-stage and B-GGG1, B-GGG2, or B-GGG3 and PSA 20-50 ng/ml. Rates of GGG4 or GGG5 and/or rates of NOC stage (≥ pT3 and/or pN1) were analyzed. Subsequently, separate univariable and multivariable logistic regression models tested for predictors of NOC stage and upgrading at RP. RESULTS: Of 486 assessable patients, 134 (28%) exhibited B-GGG1, 209 (43%) B-GGG2, and 143 (29%) B-GGG3, respectively. The overall upgrading and NOC rates were 11% and 51% for a combined rate of upgrading and/or NOC stage of 53%. In multivariable logistic regression models predicting upgrading, only B-GGG3 was an independent predictor (odds ratio [OR]: 5.29; 95% confidence interval [CI]: 2.21-14.19; p < 0.001). Conversely, 33%-66% (OR: 2.36; 95% CI: 1.42-3.95; p = 0.001) and >66% of positive biopsy cores (OR: 4.85; 95% CI: 2.84-8.42; p < 0.001), as well as B-GGG2 and B-GGG3 were independent predictors for NOC stage (all p ≤ 0.001). CONCLUSIONS: In cT1c-stage patients with high-risk PSA baseline, but low- to intermediate risk B-GGG, the rate of upgrading to GGG4 or GGG5 is low (11%). However, NOC stage is found in the majority (51%) and can be independently predicted with percentage of positive cores at biopsy and B-GGG.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".