Implementing HoLEP in an Academic Department With Multiple Surgeons in Training: Mentoring Is the Key for Success
Bibliographic record
Abstract
ObjectiveHolmium laser enucleation of the prostate (HoLEP) has been recommended for the surgical management of benign prostatic hyperplasia (BPH) in most of the international guidelines, regardless of prostatic volume. The main advantages reported by randomized clinical studies are reduced perioperative bleeding, catheterization time, and length of hospital stay, but this technique is also described as difficult to master with a steep learning curve. The objective of this study was to describe the clinical outcomes of HoLEP in the real-life setting of an academic department with multiple operators with no previous experience.MethodsA retrospective observational study was conducted including all consecutive cases performed in our department from April 2012 to October 2020. Over the study period, 31 different operators were involved. In April 2012, 2 surgeons were trained by an experienced urologist. The 29 others learned the technique progressively with the help of the first 2 surgeons (surgical mentoring).ResultsA total of 1259 patients were included. Preoperatively, the mean prostate volume and Qmax were 82.3 g and 9.4 mL/s, respectively. The mean operative time was 79.7 min. The intraoperative complication rate was 5.6% (n = 71), with the need for conversion being 0.6%. Postoperatively, the complication rate was 18.6% (n = 234). Surgeon’s experience reduced the perioperative complication rates (P = 0.01), operative time (P < 0.001), and length of hospital stay (P < 0.001), but the difference in blood transfusion rate was statistically non-significant (P = 0.3).ConclusionsMost of the 31 urologists in training were able to master HoLEP progressively, with good functional outcomes and acceptable complication rates. Supervision by trained urologists was critical for the safe dissemination of the technique in our department.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".