Local anaesthetics risks perception: A web-based survey
Bibliographic record
Abstract
Background The use of local anaesthetics (LAs) is usually associated with few adverse effects, but local anaesthetic systemic toxicity (LAST) can result in serious harm and even death. However, practitioner awareness regarding this risk has been little studied. Methods This was a closed, web-based study carried out at two Swiss university hospitals using a fully automated questionnaire. The main objective was to evaluate LAST awareness and LA use among various medical practitioners. The secondary objective was to determine whether these physicians felt that a tool designed to compute maximum safe LA doses should be developed. Results The overall participation rate was 40.2 % and was higher among anaesthesiologists (154/249, 61.8 % vs 159/530, 30.0 %; P < .001). Anaesthesiologists identified the risk of LAST and the systems involved more frequently than non-anaesthesiologists (85.1 % vs 43.4 %, P < .001). After adjusting for years of clinical experience, age, country of diploma, frequency of LA use, clinical position and being an anaesthesiologist, the only significant associations were this latter factor ( P < .001) and clinical position ( P = .016 for fellows and P = .046 for consultants, respectively). Most respondents supported the development of a tool designed to compute maximum safe LA doses (251/313, 80.2 %) and particularly of a mobile app (190/251, 75.7 %). Conclusions LAST awareness is limited among practitioners who use LAs on a regular basis. Educational interventions should be created, and tools designed to help calculate maximum safe LA doses developed. The actual frequency of unsafe LA doses administration would also deserve further study.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".