Cubital Tunnel Release Under Local and Regional Anesthesia: A Scoping Review
Bibliographic record
Abstract
Introduction: Cubital tunnel syndrome (CuTS) occurs due to compression or traction of the ulnar nerve at the elbow. When conservative management fails, CuTS release (including decompression and transposition) can be performed. Recently, more research has investigated local anesthesia (LA) and regional anesthesia (RA) for CuTS release. The objective of this scoping review was to summarize current literature on the safety and efficacy of LA and RA for CuTS release. Methods: A scoping review was conducted following the PRISMA-ScR protocol and reporting guidelines. A search was conducted of MEDLINE, EMBASE, Web of Science and CINAHL based on the key concepts of CuTS release, and LA or RA. Covidence was used for abstract and full-text screening. Results: A total of 21 studies consisting of 1385 patients and 1406 elbows were included. Most studies were case series or cohort studies. Patients received LA in 15 studies ( n = 429 elbows), RA in nine studies ( n = 616 elbows) and general anesthesia (GA) in six studies ( n = 361 elbows). Complication rates after surgery were 2.9% for LA, 2.3% for RA, and 2.5% for GA. Overnight hospital stay was more often required in GA compared to RA. One study reported significantly less postoperative pain using LA compared to GA. Four studies reported preference for LA or had high satisfaction with their procedures. Conclusions: Regional and local anesthetic techniques are safe and feasible for CuTS release. They have similar complication rates to GA, but may offer additional benefits such as intraoperative feedback, and better postoperative pain management.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".