Validating the Interval to Biochemical Failure for the Identification of Potentially Lethal Prostate Cancer
Bibliographic record
Abstract
PURPOSE: To validate the interval to biochemical failure (IBF) as a prognostic factor at the time of biochemical failure for prostate cancer mortality (PCM) following radiotherapy (RT). PATIENTS AND METHODS: From a collaborative data set of men with clinically localized prostate cancer treated with RT from four institutions in three countries, we identified 1,722 men with biochemical failure (BF; prostate-specific antigen nadir + 2 ng/mL). The IBF was defined as the time interval from completion of treatment to the date of BF. The primary outcome measure was discriminatory power in the form of the concordance index (c-index). RESULTS: Seventeen percent of men had an IBF ≤ 18 months. Median potential follow-up beyond the time of BF was 67 months. There were 290 deaths from prostate cancer. The IBF was the most discriminating individual prognostic factor overall, with a sensitivity of IBF ≤ 18 months to predict PCM within 10 years of 48.4% (95% CI, 43.3% to 54.1%); the specificity was 86.1% (95% CI, 84.5% to 87.7%), equating to a c-index of 0.611 (95% CI, 0.578 to 0.647). The 5-year cumulative incidence of PCM for IBF more than 18 months versus IBF ≤ 18 months was 9.4% (95% CI, 7.7% to 11.5%) versus 26.3% (95% CI, 21.2% to 31.8%); corresponding 10-year estimates were 26.2% (95% CI, 21.5% to 30.8%) versus 55.9% (95% CI, 48.9% to 63.0%), respectively (P < .001 for both). IBF exhibited minimal change in performance across various follow-up durations. CONCLUSION: IBF is the single most robust prognostic factor for PCM following RT without androgen deprivation therapy. This external validation demonstrates that patients and clinicians can use this information to make decisions about subsequent treatments.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".