Median lobe vs. complete gland holmium laser enucleation of the prostate: A propensity score matching
Bibliographic record
Abstract
INTRODUCTION: Benign prostatic hyperplasia (BPH) is a common condition affecting aging men. While holmium laser enucleation of the prostate (HoLEP) is one of the most effective treatments for BPH, variations of the procedure, such as median lobe HoLEP (MLHoLEP), are rarely reported. Here, we report our institution's experience with partial HoLEP. METHODS: Our institutional prospective database was queried for patients having undergone median or individual lateral lobe enucleation between 2007 and 2018. A control cohort of patients who underwent standard HoLEP (sHoLEP) was identified using 1:2 propensity score matching based on age, prostate size, maximal flow rate (Qmax), postvoid residual volume (PVR), and American Urological Association symptom score (AUAss). Three and 12-month AUAss, PVR, and Qmax were compared. RESULTS: Forty-seven patients were identified as having undergone MLHoLEP. At three-month followup, AUAss (p<0.01) and incontinence rates (p=0.045) were lower for MLHoLEP patients, in addition to them having shorter operative (36.5 mins vs. 64.5 mins, p<0.01) and enucleation (13.8 mins vs. 37 mins, p<0.01) times as compared to sHoLEP patients. No difference was noted between MLHoLEP and sHoLEP cohorts with respect to age, prostate volume, PVR, or Qmax. Significant improvement in AUAss, PVR, and Q max from baseline to three and 12 months was noted overall in both groups. CONCLUSIONS: MLHoLEP could provide a surgical option with reduced operative time, quicker improvement in AUAss, and restored continence in appropriately selected patients. Ultimately, MLHoLEP represents a safe and effective treatment option to select patients who may not be eligible for or face potential morbidity concerns associated with sHoLEP.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".