How to Minimize the Pain of Local Anesthetic Injection for Wide Awake Surgery
Bibliographic record
Abstract
After reading this article, the participant should be able to (1) almost painlessly inject tumescent local anesthesia to anesthetize small or large parts of the body, (2) improve surgical safety by eliminating the need for unnecessary sedation in patients with multiple medical comorbidities, and (3) convert many limb and face operations to wide awake surgery. We recommend the following 13 tips to minimize the pain of local anesthesia injection: (1) buffer local anesthetic with sodium bicarbonate; (2) use smaller 27- or 30-gauge needles; (3) immobilize the syringe with two hands and have your thumb ready on the plunger before inserting the needle; (4) use more than one type of sensory noise when inserting needles into the skin; (5) try to insert the needle at 90 degrees; (6) do not inject in the dermis, but in the fat just below it; (7) inject at least 2 ml slowly just under the dermis before moving the needle at all and inject all local anesthetic slowly when you start to advance the needle; (8) never advance sharp needle tips anywhere that is not yet numb; (9) always inject from proximal to distal relative to nerves; (10) use blunt-tipped cannulas when tumescing large areas; (11) only reinsert needles into skin that is already numb when injecting large areas; (12) always ask patients to tell you every time they feel pain during the whole injection process so that you can score yourself and improve with each injection; (13) always inject too much volume instead of not enough volume to eliminate surgery pain and the need for "top ups."
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".