Predictors of postoperative urinary tract infection following holmium laser enucleation of prostate
Bibliographic record
Abstract
INTRODUCTION: Storage urinary symptoms and urinary tract infection (UTI) are among the most common complications following holmium laser enucleation of the prostate (HoLEP). We aimed to study the incidence and risk factors for storage urinary symptoms and early UTI following HoLEP. METHODS: A prospectively maintained database was reviewed for patients who underwent HoLEP over a five-year period at a single tertiary center. Patient demographics, preoperative, operative, and postoperative characteristics, as well as infection rates, were obtained and analyzed using the appropriate statistical methods. RESULTS: Of a total 514 patients who underwent HoLEP, 473 patients with complete followup data were included. Mean (± standard deviation) age and median (interquartile range) prostate volume were 72±9.1 years and 89 (68-126) g, respectively. Preoperative positive urine culture and urine retention were seen in 28.5% (n=135) and 23.46 % (n=111) of patients, respectively. At six-week followup, irritative urinary symptoms were seen in 32.3% (n=153) of patients, while 13.5% (n= 64) of patients had positive urine culture. Bivariate and multivariate analysis showed that factors associated with significant higher rate of postoperative UTI at six weeks were high body mass index (BMI) (p= 0.023), weak grip strength within preoperative frailty assessment (p=0.042), positive preoperative urine culture (p=0.025), and postoperative incontinence (p=0.002). CONCLUSIONS: Storage urinary symptoms are common complaints post-HoLEP; however, it may be caused by an inflammatory rather than infective process in a significant percentage of patients. Possible predictors of UTI after HoLEP are high BMI, preoperative positive urine culture, higher frailty scale, and postoperative urinary incontinence.
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 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 | 0.000 | 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) | 0.003 | 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".