The Use of Spinal Anaesthesia for Retrograde Uretero-Renoscopy during the COVID-19 Pandemic
Bibliographic record
Abstract
Background The use of spinal anaesthesia (SA) for retrograde uretero-renoscopic surgery is considered to be not as effective as a general anaesthetic (GA) by urologists. However, there were significant concerns associated with GA both for the patient and the anaesthetic team at the height of the COVID-19 pandemic. Our unit was able to successfully transfer surgery to a purpose-built day facility that had extensive experience in delivering SA. This created the opportunity to assess the SA technique in uretero-renoscopy in a cohort of unselected patients. Objective To assess the feasibility of SA as a primary form of anaesthetic for retrograde endoluminal renal and ureteric surgery. Results Over 4 months, 41 ureteroscopic procedures were performed. The conversion rate to GA (for inadequate analgesia) was 9.8%. Surgical outcome data were compared with an equivalent cohort of patients’ who underwent GA before the pandemic. Both groups had similar outcomes: day-case discharge rate (SA 84%, GA 86%) and surgical completion rate (SA 94%, GA 90%). However, there was a difference in postoperative readmission rate (SA 8%, GA 22%) favouring SA. Conclusions This observational study demonstrated that SA is a safe and effective form of anaesthesia for uretero-renoscopic surgery, delivering non-inferior outcomes to GA. This has implications for the immediate provision of care as COVID-19 continues and as an alternative anaesthetic option to suit patients post pandemic. A larger prospective observational study would be appropriate to clearly define the benefits of SA for ureteroscopy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".