Performance Feedback May Not Improve Radical Prostatectomy Outcomes: The Surgical Report Card (SuRep) Study
Bibliographic record
Abstract
PURPOSE: Oncologic, urinary, and sexual outcomes are important to patients receiving prostate cancer surgery. The objective of this study was to determine if providing surgical report cards (SuReps) to surgeons resulted in improved patient outcomes. MATERIALS AND METHODS: A prospective before-and-after study was conducted at The Ottawa Hospital. A total of 422 consecutive patients undergoing radical prostatectomy were enrolled. The intervention was provision of report cards to surgeons. The control cohort was patients treated before report card feedback (pre-SuRep), and the intervention cohort was patients treated after report card feedback (post-SuRep). The primary outcomes were postoperative erectile function, urinary continence, and positive surgical margins. RESULTS: Baseline characteristics were similar between groups. Almost all patients (99%) were continent and the majority (59%) were potent prior to surgery. Complete 1-year followup was available for 400 patients (95%). Nerve sparing surgery increased from 70% pre-SuRep to 82% post-SuRep (p=0.01). There was a nonstatistically significant increase in the proportion of patients with a positive surgical margin post-SuRep (31% pre-SuRep vs 39% post-SuRep, p=0.08). There was no difference in postoperative erectile function (17% vs 18%, p=0.7) and a decrease in continence (75% vs 65%, p=0.02) at 1 year postoperatively. CONCLUSIONS: The SuRep platform allows accurate reporting of surgical outcomes that can be used for patient counseling. However, the provision of surgical report cards did not improve functional or oncologic outcomes. Longer durations of feedback, report card modifications, or targeted interventions are likely necessary to improve outcomes.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".