Impact of resident involvement in endoscopic bladder cancer surgery on pathological outcomes
Bibliographic record
Abstract
OBJECTIVE: Transurethral resection of bladder tumor (TURBT) pathology specimens which lack muscle are associated with clinical upstaging and may necessitate repeat resections, potentially delaying curative treatment. This study evaluated whether resident involvement in TURBT is associated with suboptimal perioperative outcomes. MATERIALS AND METHODS: All TURBTs performed at a Canadian healthcare institution from November 2011 to June 2014 were reviewed. Multivariable logistic regression models assessed associations between intraoperative resident involvement and TURBT muscle presence. Among high-risk patients (high grade, ≥ T1 or carcinoma in situ) who underwent cystectomy, time from TURBT to cystectomy was compared between resident and attending urologists with the log-rank test. RESULTS: In total, 463 TURBTs were identified. In multivariable analyses, residents were less likely to obtain muscle in specimens for all TURBTs [adjusted odds ratio (aOR) 0.59, p = 0.03] and the subset of 275 high-risk TURBTs (aOR 0.41, p = 0.006). Among patients who underwent cystectomy, time to cystectomy was delayed by a median of 23 days when residents were involved in the initial high-risk TURBT compared with attending urologists only (p = 0.024). CONCLUSIONS: In this single academic center series, intraoperative resident involvement was associated with a decreased rate of muscle presence in TURBT specimens and a prolonged time to cystectomy.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".