The effect of vibration on pain during local anaesthesia injections
Bibliographic record
Abstract
BACKGROUND: The "gate control" theory suggests pain can be reduced by simultaneous activation of nerve fibres that conduct non-noxious stimuli. This study investigated the effects of vibration stimuli on pain experienced during local anaesthetic injections. METHODS: In a preliminary study, subjects were asked to rate anticipated and actual pain from regional anaesthetic injections in the oral cavity. A second study compared, within subjects, pain from injections with and without a simultaneous vibration stimulus. Both infiltration and block anaesthetic injection techniques were assessed. In each subject, two similar injections were given and with one, a vibration stimulus was randomly allocated. Injection pain was assessed by visual analogue scale and McGill pain descriptors. RESULTS: Both infiltration and block injections were painful (mean anticipated intensity: 31.25, actual: 17.82 mm on 100 mm scale). Pain intensity with and without vibration was 12.9 mm (range 0-67) and 22.2 mm (range 0-83) respectively (p = 0.00005, paired T-test), and this effect was seen with both infiltration (p = 0.032) and block anaesthetic (p = 0.0001) injection subgroups. Furthermore, compared to no vibration-stimulus injections, injections with vibration resulted in less pain descriptors chosen (p = 0.004), and the descriptors had a lower pain rating (p = 0.001). CONCLUSIONS: The results suggest that vibration can be used to decrease pain during dental local anaesthetic administration.
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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) | 0.003 | 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".