National discrepancies in residency training of open simple prostatectomy for benign prostatic enlargement: Redefining our gold standard
Bibliographic record
Abstract
INTRODUCTION: In light of the recent Canadian Urological Association (CUA) and other urological associations' (America Urological Association, European Association of Urology) recommendations for the treatment of benign prostate hyperplasia (BPH) with lower urinary tract symptoms (LUTS), open simple prostatectomy (OSP) remains the recommended approach for large prostates with measured volumes over 80 cc. We sought to assess the current state of OSP and other BPH surgical training across Canadian urology residency programs and the use of guideline-recommended imagery prior to BPH surgery. METHODS: A survey was distributed among Canadian urology program directors in June 2019. We identified the various surgical modalities available for the treatment of BPH offered by each program and obtained the annual number of OSP performed at each academic residency program. Additionally, we evaluated if preoperative transrectal ultrasound (TRUS) of the prostate was routinely performed to obtain the prostate volume during patient counselling, as recommended by 2018 CUA guidelines. RESULTS: All 13 program directors from the Canadian urology programs responded to our survey. OSP and monopolar transurethral resection of the prostate (TURP) remain the most common across programs and are practiced in all centers. Greenlight photo-vaporization, bipolar TURP, holmium laser enucleation of the prostate, and robot-assisted simple prostatectomy were practiced in 76.8%, 69.2%, 23.1%, and 23.1% of centers, respectively. The mean number of OSP per academic training program was 4.7 cases annually. Moreover, only five (38%) academic centers routinely performed a preoperative TRUS to evaluate prostate volume for BPH counselling. CONCLUSIONS: Although recognized and referenced as the BPH gold standard for the treatment of prostates over 80 cc, Canadian urology trainees' annual OSP exposure remains extremely limited. Considering the degree of importance given (category A) to the direct observation (of a minimum of five) of this intervention during residency training in the new Royal College's practice guidelines, it may be unrealistic to reach these national standards considering the annual case OSP volumes in Canadian academic urology faculties.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".