Learning curve and functional outcomes after laser enucleation of the prostate for benign prostate hyperplasia according to surgeon’s caseload
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of surgical caseload on safety, efficacy, and functional outcomes of laser enucleation of the prostate (LEP) applying a structured mentoring program. METHODS: Patient characteristics, perioperative data, and functional outcomes were analyzed descriptively. Linear and logistic regression models analyzed the effect of caseload on complications, functional outcomes and operative speed. Within the structured mentoring program a senior surgeon was present for the first 24 procedures completely, for partial steps in procedures 25-49, and as needed thereafter. RESULTS: A total of 677 patients from our prospective institutional database (2017-2022) were included for analysis. Of these, 84 (12%), 75 (11%), 82 (12%), 106 (16%), and 330 patients (49%) were operated by surgeons at (A) < 25, (B) 25-49, (C) 50-99, (D) 100-199, and (E) ≥ 200 procedures. Preoperative characteristics were balanced (all p > 0.05) except for prostate volume, which increased with caseload. There was no significant difference in change of IPSS, Quality of life, ICIQ, pad usage, peak urine flow, residual urine, and major complications (Group A: 8.3 to E: 7.6%, p = 0.2) depending on the caseload. Caseload was not associated (Odds ratio: 0.7-1.4, p > 0.2) with major complications in the multivariable logistic regression model. Only operating time was significantly shorter with increasing caseload in the multivariable analysis (111-55 min, beta 23.9-62.9, p < 0.001). CONCLUSION: With a structured mentoring program, the safety and efficacy of LEP can be ensured even during the learning curve with very good outcome quality. Only the operating time decreases significantly with increasing experience of the surgeon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".