Impact of a teaching program on outcome quality of white light transurethral resection for bladder tumor: A cohort study
Bibliographic record
Abstract
Objective: To test the hypothesis that a teaching program improves the quality of transurethral resection of bladder tumor(TURBT) and decreases the risk of early recurrence. Material and methods: This is an observational retrospective cohort study of prospectively recorded data of patients withfirst clinical diagnosis of non-muscle-invasive bladder cancer (NMIBC), scheduled for TURBT. In 2005 a systematic TURBTteaching program was introduced in our Department. We reviewed the charts of patients who underwent TURBT in the years1998-2004, when no tutoring was applied, and those who underwent TURBT in the years 2005-2010. The outcomes of interestwere: presence/absence of detrusor muscle (DM), carcinoma in situ (CIS) detection, complication rate and recurrence rate at thefirst follow-up cystoscopy (RRFF-C). Results: Complete data from 427 patients were available: 199 before and 228 after the introduction of the teaching program.Multivariable logistic analysis showed that the training program was an independent prognostic factor for DM (presence) rate(OR = 3.92, 95%CI = 2.42-6.33), CIS detection rate (OR = 4.36, 95%CI = 1.92-9.86), and complication rate (OR = 0.28, 95%CI= 0.15-0.55), but not for RRFF-C (OR = 0.79, 95%CI = 0.52-1.20). Between 1998-2004, RRFF-C was correlated with tumornumber, pathological stage, DM presence, presence of complication, CIS detection and surgeon experience. After the introductionof the teaching program, only tumor number, DM presence and surgeon experience influenced the RRFF-C. Conclusion: Our findings suggest the hypothesis that the teaching program might have an impact of quality of TURBT, but itfailed to improve the RRFF-C.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".