Outcomes and patient tolerability of radical inguinal orchiectomy under deep intravenous sedation
Bibliographic record
Abstract
INTRODUCTION: Radical inguinal orchiectomy (RO ) is indicated for the management of testicular tumors and is universally performed under general anesthetic in the hospital. The need to perform radical orchiectomy in an expeditated fashion can result in logistical difficulties, often necessitating this procedure to happen after-hours on a semi-emergent basis. These logistical difficulties have been exacerbated by the backlog of cases from the COVID-19 pandemic. A similar procedure - inguinal hernia repair - is regularly performed under local anesthesia with minimal complications. Thus, we sought to evaluate the feasibility of performing radical orchiectomy under deep intravenous sedation in an ambulatory surgery center. METHODS: We evaluated our single-surgeon (PP), prospective database of patients who underwent RO between September 2022 and February 2023 at the Men's Health Clinic Manitoba. Patients were given a combination of deep sedation, ilioinguinal nerve block, and local anesthetic. Tolerability was assessed both perioperatively and at 4-6 weeks' followup. We reviewed the medical records for any postoperative complications. RESULTS: Eight patients underwent RO under deep sedation during the study period. All patients tolerated the surgery well and were discharged shortly after surgery. Average operative time was 40 minutes and length of stay was 46 minutes. There were no perioperative complications. CONCLUSIONS: Our pilot study demonstrates that RO can be safely and effectively performed under deep sedation. This anesthetic combination can be used both in-hospital and out-of-hospital settings, thereby resulting in faster recovery, shorter length of stay, and favorable patient and provider satisfaction.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".