180W-LBO GreenLight XPS laser vaporization for benign prostatic hyperplasia: our experience with current markers of surgical proficiency for durable and reproducible outcomes.
Bibliographic record
Abstract
INTRODUCTION: This study aims at analyzing the impact of reaching current markers of proficiency on intra and postoperative clinical outcomes of laser vaporization with 180W GreenLight XPS in the treatment of benign prostatic hyperplasia. MATERIALS AND METHODS: A retrospective analysis was conducted on a prospectively collected database of 328 consecutive patients who underwent photoselective vaporization of the prostate (PVP) using Greenlight XPS performed by a single experienced laser surgeon. A logarithmic model was used to evaluate the case number to attain benchmark criteria for durable treatment. We compared clinical outcomes before and after current markers of proficiency, defined as either an energy density of 4kJ/cm³ or a 6 month prostate-specific antigen (PSA) drop of = 50%, were attained. RESULTS: Energy delivered per prostate volume increased significantly with experience. The published benchmark values of 4kJ/cm³ and 6 month PSA drop of 50% were attained after 190 and 155 cases, respectively. There were no significant differences between groups in intraoperative complications or postoperative functional outcomes. However, the number of Clavien-Dindo category I adverse events significantly decreased with experience. Sub-analysis evaluating prostate volumes ≤ 80 cm³ and > 80 cm³ demonstrated comparable clinical outcomes before and after technical proficiency. CONCLUSION: In our experience, the case volume required to achieve consistent reference values related to durable clinical outcomes and surgical proficiency was > 150 cases. However, desirable clinical outcomes were attained before reaching current markers of proficiency, regardless of preoperative prostate size. This suggests that current thresholds of technical proficiency may not be a good predictor of satisfying clinical outcomes.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".