Validation of an RNA cell cycle progression score for predicting death from prostate cancer in a conservatively managed needle biopsy cohort
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The natural history of prostate cancer is highly variable and difficult to predict accurately. Better markers are needed to guide management and avoid unnecessary treatment. In this study, we validate the prognostic value of a cell cycle progression score (CCP score) independently and in a prespecified linear combination with standard clinical variables, that is, a clinical-cell-cycle-risk (CCR) score. METHODS: Paraffin sections from 761 men with clinically localized prostate cancer diagnosed by needle biopsy and managed conservatively in the United Kingdom, mostly between 2000 and 2003. The primary end point was prostate cancer death. Clinical variables consisted of centrally reviewed Gleason score, baseline PSA level, age, clinical stage, and extent of disease; these were combined into a single predefined risk assessment (CAPRA) score. Full data were available for 585 men who formed a fully independent validation cohort. RESULTS: In univariate analysis, the CCP score hazard ratio was 2.08 (95% CI (1.76, 2.46), P<10(-13)) for one unit change of the score. In multivariate analysis including CAPRA, the CCP score hazard ratio was 1.76 (95% CI (1.44, 2.14), P<10(-6)). The predefined CCR score was highly predictive, hazard ratio 2.17 (95% CI (1.83, 2.57), χ(2)=89.0, P<10(-20)) and captured virtually all available prognostic information. CONCLUSIONS: The CCP score provides significant pretreatment prognostic information that cannot be provided by clinical variables and is useful for determining which patients can be safely managed conservatively, avoiding radical treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it