The Wide-Awake Approach to Dupuytren's Disease: Fasciectomy under Local Anesthetic with Epinephrine
Bibliographic record
Abstract
The Wide-Awake Approach to Dupuytren's contracture involves fasciectomy under local anesthetic with epinephrine and no tourniquet. The goal of this study is to show that the Wide-Awake Approach produces equivalent outcomes to fasciectomy under general anesthetic with a tourniquet, with fewer risks to the patient. A multicenter retrospective review was conducted on 111 patients with fasciectomies under local or general anesthetic between 2001 and 2007. Data on patient demographics, comorbidities, cost, as well as range of motion was collected and evaluated using Microsoft Excel and SAS. Of 148 fingers, 102 were treated under local and 46 under general anesthetic. The average postoperative Total Active Motion (TAM) for general anesthetic patients was 199.0 ± 29.6 (D5), 223.9 ± 29.3 (D4), 234.6 ± 14.6 (D3), and 246.7 ± 14.4 (D2). The average postoperative TAM for local anesthetic patients was 168.3 ± 62.2 (D5), 195.9 ± 67.5 (D4), 173.0 ± 72.6 (D3), and 177.5 ± 31.8 (D2). There were no significant differences between any of these individual groups (p = 0.09, 0.26, 0.12, and 0.20, respectively); however, when pooled, the overall TAM was significantly greater in the general anesthesia group (222.0 ± 29.7 vs. 186.0 ± 63.0, p = 0.002.). Complication rates and types were similar with both techniques. The Wide-Awake Approach to Dupuytren's contracture avoids general anesthetic risks and has cost benefits to healthcare providers. Although it yields similar range of motion outcomes to fasciectomy performed under general anesthesia, total active motion may be better with fasciectomy done under general anesthesia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".