Current Trends in Use of Epinephrine in Hand Surgery
Bibliographic record
Abstract
Background: Epinephrine use during hand surgery has been stigmatized due to a fear of digital necrosis. Clinical experience in the past 2 decades has shown epinephrine in local anesthetic to be safe. We sought to analyze the use of epinephrine among hand surgeons and identify variables associated with it. Methods: A deidentified 21-question survey was distributed via email to the 914 and 415 members of the American Association for Hand Surgery and the Canadian Society for Surgery of the Hand, respectively. Questions included residency type, years of practice, practice setup/ownership, practice leadership, usage of epinephrine, availability of reversal agents, and reasons for or against usage. Results: Of 188 responders, 170 (90%) used epinephrine in local anesthetic for hand surgery procedures. By nationality, 100% (43) of Canadian surgeons and 89% (108) of US surgeons use epinephrine ( P = .01). Among surgeons with practice ownership, 88% (102) used epinephrine compared with 93% (85) of those surgeons that we employed ( P = .28). Comparing surgeons with teaching responsibilities versus those without training responsibilities showed that surgeons who did not teach used epinephrine at a higher rate (87% vs 98%, P = .04). In addition, plastic surgery–trained surgeons (111) used epinephrine in 97.2% of cases while orthopedic surgery–trained surgeons (57) used epinephrine in 80.2% of cases ( P = .0003). No difference was found when examining the use of epinephrine and surgeon age ( P = .28). Conclusions: Most respondents believe that epinephrine is safe. Training background, location, and practice setup are significant factors in the use of epinephrine, whereas practice ownership and physician age are not major factors.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".