The safety and efficacy of ambulatory urologic surgery
Bibliographic record
Abstract
INTRODUCTION: Amid substantial surgical wait lists, novel methods are needed to improve the delivery of surgical care in Canada. One strategy involves shifting select surgeries from hospitals into community ambulatory centers, which expedite procedures and allow hospitals to prioritize critical and complex patients. We sought to evaluate surgical outcomes at a novel, Canadian urologic clinic and surgical center. METHODS: A retrospective study was conducted at a novel, accredited surgical facility and outpatient ambulatory clinic from August 2022 to August 2023. Procedures ranged from scrotal and transurethral surgeries to inflatable penile prosthesis insertion. Traditional outpatient procedures, including vasectomy and cystoscopy, were excluded. All patients were discharged the same day and seen 4-6 weeks post-procedure. Variables of interest included surgery type, anesthesia administered, additional clinic appointments, unplanned family physician appointments, visits to the emergency department (ED), and hospital admissions. RESULTS: In a 12-month period, 519 surgeries were performed. The mean patient age was 49.6±17.3 years, with most classified as American Society of Anesthesiologists (ASA) 1-2 (88.8%). Most (95.8%, n=497) patients did not require medical care outside the clinic before their scheduled followup; 2.5% (n=13) visited the ED presenting for wound concerns, postoperative pain, query infection, or catheter-related concerns. Only 1.7% (n=9) required an unscheduled appointment with their family physician, with concerns being inadequate postoperative pain management (n=4) or suspected infection (n=4). No patient required hospital admission. CONCLUSIONS: Many urologic surgeries classically performed in hospital operating rooms can be safely performed in a non-hospital, outpatient surgical facility with preservation of good outcomes. This strategy can potentially improve the efficiency of urologic healthcare delivery in select patients.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".