Role of survivin expression in predicting biochemical recurrence after radical prostatectomy: a multi‐institutional study
Bibliographic record
Abstract
OBJECTIVE: To assess the association of survivin expression with clinicopathological features and biochemical recurrence (BCR) after radical prostatectomy (RP) in a large multi-institutional cohort. METHODS: Survivin expression was evaluated by immunohistochemistry on a tissue microarray of RP cores from 3 117 patients. Survivin expression was considered altered when at least 10% of the tumour cells stained positive. The association of altered survivin expression with BCR was evaluated using Cox proportional hazards regression models. RESULTS: Survivin expression was altered in 1 330 patients (42.6%). Altered expression was associated with higher Gleason score on RP (P = 0.001), extracapsular extension (P = 0.019), seminal vesicle invasion (P < 0.001) and lymph node metastases (P = 0.009). The median (interquartile range) follow-up was 38 (21-66) months. Patients with altered survivin expression had a shorter BCR-free survival time than those with normal expression (5-year BCR-free survival estimates: 74.7 vs 79.0%; P = 0.008). Altered survivin expression did not retain its prognostic value, however, after adjustment for the effect of established clinicopathological factors (P = 0.73). Subgroup analyses also showed no independent prognostic value of survivin. CONCLUSIONS: Survivin expression is commonly altered in patients undergoing RP. Altered survivin expression is associated with the clinicopathological features of biologically and clinically aggressive PCa. Survivin expression was associated with BCR only in univariable analysis, limiting its value in daily clinical decision-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".