Modeling Prostate Specific Antigen Kinetics in Patients on Active Surveillance
Bibliographic record
Abstract
PURPOSE: Prostate specific antigen doubling time was used to stratify patients into groups at low and high risk for progression. The prostate specific antigen kinetics in these 2 groups were modeled. MATERIALS AND METHODS: In this prospective, single-arm cohort study patients with favorable clinical parameters (stage T1b-T2b N0M0, Gleason score 7 or less, prostate specific antigen 15 ng/ml or less) were conservatively treated with watchful waiting. Evolution of serial prostate specific antigen measurements over time was estimated from a general linear mixed model of the natural log of prostate specific antigen. The corresponding average and individual prostate specific antigen doubling times were also calculated. RESULTS: Since November 1995 a total of 231 patients had at least 6 months of followup and at least 3 prostate specific antigen measurements. Based on prostate specific antigen doubling time and repeat biopsy, 93 patients fulfilled the criteria for high risk of disease progression and 138 were defined as low risk. Given the baseline status of these individuals, 2 reference average lines (high risk and low risk) were derived to model the evolution of prostate specific antigen levels and permit more rational decision making regarding the need for definitive intervention. The average prostate specific antigen doubling time was 2.97 years (95% CI 2.2-4.4) in patients allocated to the high risk group and 6.54 years (95% CI 4.8-12.3) in those at low risk. CONCLUSIONS: By applying the dynamic prognostic rule in combination with serial biopsy, a rational decision for definitive intervention based on the risk of disease progression could be optimally recommended about 2.3 years after initiated surveillance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".