Plasmakinetic vaporization versus plasmakinetic resection to treat benign prostatic hyperplasia: A prospective randomized trial with 1 year follow-up
Bibliographic record
Abstract
INTRODUCTION: We evaluate the efficacy and outcomes of plasma-kinetic vaporization (PKVP) and plasmakinetic resection (PKR) to treat benign prostatic hyperplasia (BPH). METHODS: A total of 183 patients with BPH underwent plasma-kinetic prostatic surgery between 2008 and 2012 at Kars State Hospital and Kafkas University Faculty of Medicine, Turkey. After clinical and preoperative evaluation, the patients were randomized to PKRP or PKVP groups sequentially by using computer-generated numbers. Group 1 included 96 patients treated with PKR. Group 2 included 87 patients treated with PKVP. Patients in both groups were compared in terms of hemoglobin drop, operation time, catheter duration, reobstruction, incontinence and recatheterization. RESULTS: When we compared the maximum flow rates (Qmax values) at the 12th month, there was no statistical difference between 2 groups. Group 1 had a mean Qmax value of 17.92 ± 3.819 and Group 2 had a 18.15 ± 3.832 value (p > 0.05). There was a statistical difference between the groups in terms of hemoglobin drop, catheter duration and operation time. The mean catheter duration in Group 1 was 3.74 ± 1.049 days, and in Group 2 it was 2.64 ± 0.849 days (p < 0.05). Operation time was statistically longer in Group 2 (PKVP) and hemoglobin drop was statistically higher in Group 1 (PKR). CONCLUSION: PKVP for BPH is safe and effective. When compared with PKRP, it provides a significantly shorter catheter duration and less bleeding due to hemostasis control with similar IPSS and Qmax improvements after 1 year.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".