A pretreatment nomogram predicting biochemical failure after salvage cryotherapy for locally recurrent prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To gather a pooled database from six tertiary-care referral centres using salvage cryotherapy (SC) for locally recurrent prostate cancer, and develop a pretreatment nomogram allowing a prediction of the probability of biochemical failure after SC, based on pretreatment clinical variables. PATIENTS AND METHODS: We retrospectively analysed 797 men treated at six tertiary-care referral centres with SC for locally recurrent disease after primary radiotherapy with curative intent. The median duration of follow-up from the time of SC to the date of last contact was 3.4 years. The primary study endpoint was biochemical failure, defined as a serum prostate-specific antigen (PSA) level after SC of >0.5 ng/mL. RESULTS: Overall, the rate of biochemical failure was 66% with a median of 3.4 years of follow-up. A logistic regression model was used to predict biochemical failure. Covariates included serum PSA level at diagnosis, initial clinical T stage, and initial biopsy Gleason score. On the basis of these results, a pretreatment nomogram was developed which can be used to help select patients best suited for SC. Our pretreatment nomogram was internally validated using 500 bootstrap samples, with the concordance index of the model being 0.70. CONCLUSION: A pretreatment nomogram based on several diagnostic variables (serum PSA level at diagnosis, biopsy Gleason grade, and initial clinical T stage) was developed and might allow the selection of ideal candidates for SC.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".