Minimizing the Pain of Local Anesthesia Injection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it