Surgical volume is related to the rate of positive surgical margins at radical prostatectomy in European patients
Bibliographic record
Abstract
OBJECTIVE: To assess the association between surgical volume (SV) and the rate of positive surgical margins (PSM) after radical prostatectomy (RP) in a large single-institution European cohort of patients. PATIENTS AND METHODS: In all, 2402 men had a RP by a group of 11 surgeons, all of whom were trained by the surgeon with the highest SV; all surgeons used the same surgical technique. Variables assessed before RP were prostate-specific antigen (PSA) level, clinical stage and biopsy Gleason sum; variables assessed after RP were PSA level, extracapsular extension, seminal vesicle invasion, lymph node invasion and pathological Gleason sum. These were used to predict the rate of PSM in models before or after RP. Multivariate models were complemented with SV to test its independent and multivariate statistical significance and to quantify its impact on the model's overall (and 200 bootstrap-corrected) predictive accuracy. RESULTS: The mean (range) SV was 201 (1-1293) RPs; the mean (median, range) rate of PSM was 20.2 (21.4, 0-32.9)%. In multivariate models, SV was a highly statistically significant independent predictor of PSM (P < 0.001) and increased the predictive accuracy in multivariate models both before (2.0%) and after RP (1.5%, both P < 0.001). However, when the surgeon with the highest SV, who contributed to 1293 cases, was removed from the analyses, the multivariate independent prediction and the gains in predictive accuracy related to adding SV, disappeared in the models both before (P = 0.9, accuracy gain 0.1%) and after (P = 0.4, accuracy gain - 0.3%) RP. CONCLUSIONS: These results indicate that patients treated by surgeons with a very high volume can expect to have a significantly lower rate of PSM, after accounting for clinical and pathological case-mix differences. However, SV is not a predictor of PSM when analyses are restricted to intermediate- and low-volume surgeons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".