Pain and Efficacy Rating of a Microprocessor-Controlled Metered Injection System for Local Anaesthesia in Minor Hand Surgery
Bibliographic record
Abstract
Purpose. Little attention has been given to syringe design and local anaesthetic administration methods. A microprocessor-controlled anaesthetic delivery device has become available that may minimize discomfort during injection. The purpose of this study was to document the pain experience associated with the use of this system and to compare it with use of a conventional syringe. Methods. A prospective, randomized clinical trial was designed. 40 patients undergoing carpal tunnel release were block randomized according to sex into a two groups: a traditional syringe group and a microprocessor-controlled device group. The primary outcome measure was surgical pain and local anaesthetic administration pain. Secondary outcomes included volume of anaesthetic used and injection time. Results. Analysis showed that equivalent anaesthesia was achieved in the microprocessor-controlled group despite using a significantly lower volume of local anaesthetic (P = .0002). This same group, however, has significantly longer injection times (P < .0001). Pain during the injection process or during surgery was not different between the two groups. Conclusions. This RCT comparing traditional and microprocessor controlled methods of administering local anaesthetic showed similar levels of discomfort in both groups. While the microprocessor-controlled group used less volume, the total time for the administration was significantly greater.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".