Bibliographic record
Abstract
Clinical outcomes in patients with localized prostate cancers are heterogeneous. In recent years, analyses of large datasets from multiple centres have yielded a better understanding of how to measure risk in localized prostate cancer. Regardless of whether patients are treated with prostatectomy, radiotherapy, brachytherapy, or expectant management, three factors appear correlated with clinical outcome: biopsy Gleason score, clinical T stage, and serum prostate-specific antigen (PSA). Partin Tables, derived from these parameters and recently updated and refined, may be used to estimate the risk of metastasis and to assess certain aspects of surgical management in clinically localized disease. Partin tables, however, are limited by the fact that pathologic stage does not always predict clinical outcome. Nomograms that employ serum PSA, biopsy Gleason score, and clinical T-stage have been developed with the aim of predicting clinical recurrence after radical prostatectomy or radiation therapy. Three risk categories for clinically localized prostate cancer have recently been developed by the Canadian Genito-Urinary Radiation Oncologists Consensus Conference, which group cases according to serum PSA, T-stage, and biopsy Gleason score. Additional factors have been assessed in the hopes of improving the prediction of outcome in clinically localized disease, but none of these has consistently been demonstrated to add independent value to the principal parameters of serum PSA, T-stage, and Gleason score. Virtually all predictive nomograms, algorithms, and tables incorporate a combination of these three parameters. While these tools may be useful in prognosticating an individual case, several limitations preclude their widespread use. The greatest benefit to date of risk stratification is its use in comparing outcomes of series of patients treated with various modalities, and in clinical trial design and analysis.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".