Time to Detectable Metastatic Disease in Patients with Rising Prostate-Specific Antigen Values following Surgery or Radiation Therapy
Bibliographic record
Abstract
PURPOSE: To determine factors associated with the development of radiographic metastatic progression for patients with recurrent prostate cancer following surgery and/or radiation therapy with prostate-specific antigen (PSA) doubling times of <12 months. EXPERIMENTAL DESIGN: One hundred and forty-eight patients with rising PSA values after primary therapy and a PSA doubling time of <12 months enrolled on clinical protocols were followed and monitored at protocol-specified intervals with examinations, PSA determinations, and imaging studies that included a computed tomography or magnetic resonance imaging and bone scan until metastases were detected. Metastasis-free survival was estimated using the Kaplan-Meier method and factors predictive of progression-free survival were estimated using the proportional hazards model. A nomogram based on the Cox model was constructed. RESULTS: Metastatic events were documented in 74% (110 of 148) of patients during the follow-up period. The median progression-free survival was 19 months, with 3- and 5-year metastatic progression-free survival of 32% and 16%, respectively. T stage (P=0.07) and Gleason grade (P=0.006) at the time of diagnosis, PSA values at the time of protocol entry (P<0.001), and PSA doubling time (P<0.001) were associated with progression in univariate analysis. These were combined into a nomogram to assess risk for an individual patient. CONCLUSIONS: Tumor characteristics at the time of diagnosis, PSA doubling time following relapse, and the PSA value at the time of the protocol are predictive of metastatic progression. Because the PSA value at the time of monitoring was predictive, early treatment to prevent metastatic progression is favored.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".