Thulium Laser Enucleation of the Prostate (ThuLEP) as a Technique for Treatment of BPH: Evaluation of a Six-Year Experience at a Single Institution
Bibliographic record
Abstract
Background and objective Transurethral resection of the prostate (TURP) has been the undisputed reference standard for elderly men with lower urinary tract symptoms (LUTS) caused by benign prostatic enlargement (BPE). However, morbidity after TURP remains significant, with increased risks of bleeding and TUR syndrome. In recent years, there has been a gradual increase in the role of laser technology for the treatment of symptomatic BPE. The aim of our study was to evaluate the efficacy and safety of the novel technique Thulim laser enucleation of prostate (ThuLEP) developed for the first time in 2009 by Imkamp et al. in treating symptomatic BPE. Materials and methods Analysis of the data from electronic records, case notes, clinic letters all the patients who had undergone ThuLEP using a 70 W thulium laser (Revolix) for symptomatic outflow obstruction over a 6-year period was done. We looked at the pre-procedure PSA, maximum urinary flow rate (Qmax), Post void residual (PVR) and haemoglobin, comparing it with the postoperative haemoglobin. Results Two-hundred twenty-two patients underwent ThuLEP procedure performed by 2 surgeons. We found a 159 % improvement in Q max, a 61 % improvement in PVR. No mortalities occurred and only 1 patient required post operative blood transfusion. ThuLEP represents a safe, effective surgical option in patients with symptomatic BPH with relatively little complications, producing significant improvements in both Qmax and PVR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".