Minimizing the Pain of Local Anesthesia Injection
Bibliographic record
Abstract
BACKGROUND: Local anesthetic injection is often cited in literature as the most painful part of minor procedures. It is also very possible for all doctors to get better at giving local anesthesia with less pain for patients. The purpose of this article is to illustrate and simplify how to inject local anesthesia in an almost pain-free manner. METHODS: The information was obtained from reviewing the best evidence, from an extensive review of the literature (from 1950 to August of 2012) and from the experience gained by asking over 500 patients to score injectors by reporting the number of times they felt pain during the injection process. RESULTS: The results are summarized in a logical stepwise pattern mimicking the procedural steps of an anesthetic injection-beginning with solution selection and preparation, followed by equipment choices, patient education, topical site preparation, and finally procedural techniques. CONCLUSIONS: There are now excellent techniques for minimizing anesthetic injection pain, with supporting evidence varying from anecdotal to systematic reviews. Medical students and residents can easily learn techniques that reliably limit the pain of local anesthetic injection to the minimal discomfort of only the first fine needlestick. By combining many of these conclusions and techniques offered in the literature, tumescent local anesthetic can be administered to a substantial area such as a hand and forearm for tendon transfers or a face for rhytidectomy, with the patient feeling just the initial poke.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".