Achieving the Optimal Epinephrine Effect in Wide Awake Hand Surgery using Local Anesthesia without a Tourniquet
Bibliographic record
Abstract
BACKGROUND: In our experience, for all surgeries in the hand, the optimal epinephrine effect from local anesthesia-producing maximal vasoconstriction and visualization-is achieved by waiting significantly longer than the traditionally quoted 7 min from the time of injection. METHODS: In this prospective comparative study, healthy patients undergoing unilateral carpal tunnel surgery waited either 7 min or roughly 30 min, between the time of injection of 1 % lidocaine with 1:100,000 epinephrine and the time of incision. A standardized incision was made through dermis and into the subcutaneous tissue followed by exactly 60 s of measuring the quantity of blood loss using sterile micropipettes. RESULTS: There was a statistically significant reduction in the mean quantity of bleeding in the group that waited roughly 30 min after injection and before incision compared to the group that waited only 7 min (95 % confidence intervals of 0.06 + -0.03 ml/cm of incision, compared to 0.17 + -0.08 ml/cm, respectively) (P = 0.03). CONCLUSIONS: Waiting roughly 30 min after injection of local anesthesia with epinephrine as oppose to the traditionally taught 7 min, achieves an optimal epinephrine effect and vasoconstriction. In the hand, this will result in roughly a threefold reduction in bleeding-making wide awake local anesthesia without tourniquet (WALANT) possible. This knowledge has allowed our team to expand the hand procedures that we can offer using WALANT. The benefits of WALANT hand surgery include reduced cost and waste, improved patient safety, and the ability to perform active intraoperative movement examinations.
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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 | 0.002 |
| 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".