Assessment of Antibiotic Prophylaxis Prescribing Patterns for TURP: A Need for Canadian Guidelines?
Bibliographic record
Abstract
BACKGROUND: While antibiotic prophylaxis is recommended to all patients undergoing transurethral resection of prostate (TURP), little data exist regarding prescribing patterns of urologists prior to this procedure. Here, we sought to determine real-world antibiotic prophylaxis prescribing patterns at a high volume Canadian institution and determine compliance rates to recommendations put forth by the American Urological Association's (AUA) Best Practice Statement (BPS) on antimicrobial prophylaxis. METHODS: A retrospective chart review of 488 patients undergoing TURP was conducted. Electronic medical records were reviewed to determine antibiotics prescribed 3 hours preoperatively and 24 hours postoperatively. For patients without a catheter, compliance was defined as those receiving an antibiotic prior to TURP. In patients with an indwelling catheter, compliance was defined as those receiving antibiotics from two different classes prior to surgery. RESULTS: Overall, a total of 30 antibiotic regimens were utilized. The most common single antibiotic regimens prescribed were ciprofloxacin (32%), cefazolin (25%) and gentamicin (3%). In those patients with indwelling Foley catheters prior to TURP, a significant increase in gentamicin, as well as combination antibiotic regimens, was noted. The compliance rate with the AUA BPS in patients without a preoperative catheter was 81%, while the compliance rate for patients with an indwelling catheter prior to TURP was 37%. INTERPRETATION: Collectively, our results demonstrate that prescribing patterns vary significantly prior to TURP, with compliance to AUA BPS being lower than anticipated. Overall, these results support educational efforts in this area, and the development of Canadian recommendations to improve uptake by practicing urologists.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".