Carpal Tunnel Release without a Tourniquet: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Open carpal tunnel release is commonly performed with the use of a tourniquet. The combination of local anesthetic and epinephrine with a pneumatic tourniquet helps provide clear visualization during decompression of the median nerve. There has been a rapid expansion of literature challenging the use of tourniquets in open carpal tunnel release. Consequently, the local anesthesia/no tourniquet approach has become increasingly popular. The authors evaluated the outcomes of awake open carpal tunnel release with and without a tourniquet. METHODS: The authors attempted to identify all relevant studies, regardless of language or publication status. A systematic database search for relevant studies was conducted in MEDLINE, EMBASE, EBSCO, and CENTRAL. Included studies compared patients undergoing awake open carpal tunnel release with and without an arm or forearm tourniquet. RESULTS: Eight studies evaluating 765 patients and 866 hands were included. Open carpal tunnel release with the wide awake, local anesthesia, no tourniquet approach resulted in a 2.14 point reduction on the visual analog scale (95% CI, 1.30 to 2.98; p < 0.001). The procedure was 1.82 minutes faster with the use of a tourniquet (95% CI, -3.26 to -0.39; p = 0.01). There were no significant differences between groups in intraoperative blood loss, surgeon perceived difficulty, and complications. CONCLUSION: This systematic review found that tourniquet use causes significantly more pain with no significant clinical benefit as compared with using a wide awake, no tourniquet approach in carpal tunnel decompression.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".