PD08-06 RISK PREDICTION TOOL FOR GRADE RECLASSIFICATION IN FAVORABLE-RISK MEN ON ACTIVE SURVEILLANCE
Bibliographic record
Abstract
cancer research international: active surveillance (PRIAS), an American cohort (from Johns Hopkins), and a Canadian cohort (from Sunnybrook).These cohorts contained 5,129 follow-up biopsies in 3,412 PC patients.Calibration of the risk calculator was assessed graphically and the discriminative ability was quantified using the area under the receiver operating characteristic curve (AUC).Clinical usefulness was assessed using decision curves analysis (DCA).RESULTS: The AUC at external validation was 0.64 in PRIAS, 0.73 in the American cohort and 0.66 in the Canadian cohort.The probability of upgrading or risk reclassification was somewhat overestimated in PRIAS and the Canadian cohort and somewhat underestimated in the American cohort.DCA showed that the model was clinical useful in each cohort for risk thresholds between 10% and 30%.CONCLUSIONS: The risk calculator provided moderate discrimination in 3 cohorts of the GAP-3 consortium, but showed some miscalibration.Before applying the risk calculator in clinical practice recalibration may be required. Source of Funding: Submitted on behalf of the
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".