Assessing peri-operative antibiotic administration practices amongst urologic surgeons performing holmium laser enucleation of the prostate worldwide
Bibliographic record
Abstract
PURPOSE: Holmium Laser Enucleation of the Prostate (HoLEP) is a size-independent surgical treatment for benign prostatic hypertrophy. There is currently a lack of data on peri-operative antibiotic prescribing patterns for HoLEP and, thus, no consensus on optimal practices. This study aims to assess peri-operative antibiotic prescribing practices for HoLEP. METHODS: Members of the Endourological Society (EUS) were invited by e-mail to complete a REDCap survey. The survey inquired about surgeons' practice setting, training, surgical volume, antibiotic prescribing practices and explored different factors that might affect antibiotic choice and duration. A p-value of < 0.05 was determined to be statistically significant. RESULTS: A total of 70 Urologists (66 male, 4 female) reported that they performed an average of 108 HoLEPs per year with a mean clinical experience of 11 years. In the case of a negative pre-operative urine culture with a patient who is not catheterized/intermittently self-catheterizing (C/ISC), 96% of urologists would only give a single peri-operative dose of antibiotic. If the patient is C/ISC then 49% of Urologists would give more than a single dose of peri-operative antibiotic when the urine culture is negative. If the pre-operative urine culture is negative, 39% of surgeons would prescribe post-operative antibiotics even when the patient is not C/ISC and this increased to 64% if the patient is C/ISC. The most common factors urologists considered when prescribing antibiotic prophylaxis/therapy were positive urine culture, catheterization status, and a history of recurrent UTIs. Non-academic urologists administered post-operative prophylaxis more often (p < 0.05) and urologists with more experience treated a positive urine culture for a shorter period. CONCLUSION: There is significant variability for peri-operative antibiotic prescribing practices prior to HoLEP. In general, more antibiotics are prescribed if the patient has a history of C/ISC or infection. Further clinical studies are needed to identify optimal antibiotic prescribing protocols prior to HoLEP.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".