Medication errors in veterinary anesthesia: a literature review
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of medication errors (MEs) in veterinary medicine, with a focus on the perianesthetic period; to compare MEs in veterinary medicine with human anesthesia practice, and to describe factors contributing to the risk of MEs and strategies for error reduction. DATABASES USED: PubMed and CAB abstracts; search terms: [("patient safety" or "medication error∗") AND veterin∗]. CONCLUSIONS: Human anesthesia is recognized as having a relatively high risk of MEs. In veterinary medicine, MEs were among the most commonly reported medical error. Predisposing factors for MEs in human and veterinary anesthesia include general (e.g. distraction, fatigue, workload, supervision) and specific factors (e.g. requirement for dose calculations when dosing for body mass, using several medications within a short time period and preparing syringes ahead of time). Data on MEs are most commonly collected in self-reporting systems, which very likely underestimate the true incidence, a problem acknowledged in human medicine. Case reports have described a variety of MEs in the perianesthetic period, including prescription, preparation and administration errors. Dogs and cats were the most frequently reported species, with MEs in cats more commonly associated with harmful outcomes compared with dogs. In addition to education and raising awareness, other strategies described for reducing the risk of MEs include behavioral, communication, identification, organizational, engineering and cognitive aids.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".