Ureteroscopy under conscious sedation: A proof-of-concept study
Bibliographic record
Abstract
INTRODUCTION: Ureteroscopy (URS) is commonly performed under general anesthesia (GA) to maximize patient tolerability and minimize surgical complications; however, given the improvements in endoscopic technology and risks associated with GA, alternate forms of anesthesia have been postulated. We aimed to evaluate the outcomes of URS under conscious sedation. METHODS: We completed a retrospective cohort study from November 2019 to June 2020 at a tertiary-level hospital. All URSs that were performed under urologist-directed conscious sedation were included. Our primary outcome was the ability to complete URS, defined as success rate. Secondary outcomes included: stone-free rate, intraoperative complication rate, hospital admission rate, and sedation requirement. Univariate- and multivariate-adjusted logistic regression analyses were employed. RESULTS: Ninety-nine URSs were included. Most (73/99, 73.7%) were performed for urolithiasis. The overall success rate was 83.8% (83/99), with 81.0% (34/42) intra-renal and 70.0% (16/23) proximal ureter success rates. The stone-free rate was 80.8% (59/73). No intraoperative complications nor hospital admissions were reported. The mean amount of sedation required was 3 mg (interquartile range [IQR] 2-4] of midazolam and 100 μg (100-150) of fentanyl. On multivariate analysis, midazolam was significantly associated with increased success (odds ratio 2.496, 95% confidence interval 1.057-5.892, p=0.037). CONCLUSIONS: We have shown that proximal and intrarenal URS under conscious sedation is safe and effective. We were limited by our lack of followup, small sample size, selection bias to chose healthy patients, and lack of patient tolerability data. Patients and healthcare systems may benefit from implementing this innovation more broadly.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".