Predicting outcomes in patients with urologic cancers
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the available predictive and prognostic models addressing oncological outcomes in patients with bladder, kidney and prostate cancer. RECENT FINDINGS: A systematic review of the English literature on the discussed topics was performed. All manuscripts were retrieved from PubMed, and restricted to entries with an abstract. Keywords were 'diagnosis', 'stage', 'prognosis' and 'nomograms' for bladder, kidney and prostate cancer, respectively. Of these, 70 were selected for inclusion, based on content, clinical relevance, quality, level of evidence, and year of publication. SUMMARY: We identified six models for prediction of the natural history of treated bladder cancer. We report on 15 models for patients with kidney cancer. Of these, two preoperative prognostic models predict recurrence-free survival, three postoperative models address disease recurrence, five postoperative models predict disease-specific survival, and, finally, five models predict overall survival in patients with metastatic kidney cancer. For prostate cancer, we found eight models predicting biopsy outcome, 17 models for pretreatment prediction of pathologic stage of clinically localized disease, eight models for prediction of biochemical recurrence, and, finally, six models predicting cancer control outcomes in relapsed or hormone-refractory metastatic prostate cancer. In patients with urologic malignancies, cancer control outcomes can be predicted in a highly accurate and evidence-based fashion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".