Teaching medical students and residents how to inject local anesthesia almost painlessly
Bibliographic record
Abstract
The objective of the present study was to determine whether it is possible to consistently and reliably teach medical students and resident learners how to administer local anesthetics in an almost painless manner. Using the published technique, 25 consecutive medical students and residents were taught how to inject local anesthetics for carpal tunnel release by watching the senior author perform the technique once. The learner then independently administered the anesthesia to the next patient who then scored the learner's ability to inject the local anesthetic from a pain perspective. The teaching technique is demonstrated in an accompanying online video. The learners were consistently capable of administering local anesthetics with minimal pain. During the injection process, the patients only felt pain once ('hole-in-one') 76% of the time. This pain was attributed to the first 27-gauge needle poke. The other 24% of the time, patients felt pain twice (eagle) during the 5 min injection process. All 25 patients rated the entire pain experience to be less than 2/10. Eighty-four per cent of the patients indicated that the experience was better than local anesthetic given at the dentist's office. Medical students and residents can quickly and reliably learn how to administer local anesthesia for carpal tunnel release with minimal pain to the patient.
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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 | 0.004 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".