Trigger Finger Release Using Wide-Awake Local Anesthesia No Tourniquet Versus Local Anesthesia With a Tourniquet: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Trigger finger release (TFR) is a common hand surgery, historically performed using a tourniquet. Recently, wide-awake local anesthesia no tourniquet (WALANT) has gained popularity due to ostensible advantages such as improved patient pain, satisfaction, lower rate of complications, and decreased cost. This systematic review compares outcomes of WALANT for TFR with local anesthesia with a tourniquet (LAWT). MEDLINE, Embase, CINAHL, Web of Science, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched. All English-language peer-reviewed randomized and observational studies assessing TFR in adults were included. Quality of evidence was assessed with the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. Eleven studies (4 randomized controlled trials, 7 observational) including 1233 patients were identified. In the WALANT group, pain on injection was statistically nonsignificantly lower (mean difference [MD]: −1.69 points, 95% confidence interval [CI] = −4.14 to 0.76, P = .18) and postoperative pain was statistically lower in 2 studies. Patient and physician satisfaction were higher and analgesic use was lower in WALANT. There were no significant differences between groups for functional outcomes or rates of adverse events. Preoperative time was longer (MD: 26.43 minutes, 95% CI = 15.36 to 37.51, P < .01), operative time similar (MD: −0.59 minutes, 95% CI = −2.37 to 1.20, P = .52), postoperative time shorter (MD: −27.72 minutes, 95% CI = −36.95 to −18.48, P < .01), and cost lower (MD: −52.2%, 95% CI = −79.9% to −24.5%) in WALANT versus LAWT. The GRADE certainty of evidence of these results ranges from very low to low. This systematic review does not confirm superiority of WALANT over LAWT for TFR due to moderate to high risk of bias of included studies; further robust trials must be conducted.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".