Incidence and type of voluntary reported perianesthetic medication errors in community veterinary clinics in Calgary, Canada
Bibliographic record
Abstract
OBJECTIVE: To collect medication error (ME) data during the perianesthetic period from small animal clinics. SAMPLE: 6 small animal general practice veterinary clinics. METHODS: Small animal general practice veterinary clinics were recruited in this prospective observational study, with staff given a presentation on medical errors and instructed on how to submit medication error reports to an online reporting system. Errors were classified according to type and timing. RESULTS: A total of 2,728 general anesthesia or sedation procedures were performed, with 49 ME reports submitted. One duplicated report of the same error was excluded, resulting in a ME rate of 1.8%. Most reports (69% [33/48]) were near misses. The remaining 31% were MEs that reached the patient but did not cause harm. Wrong dose errors were the most common type (63% [30/48]), of which 80% (24/30) were calculation errors. Premedication/sedation and maintenance were the most reported stages, at 47% (20/43) and 23% (10/43), respectively. None of the MEs reported resulted in an adverse event, with an approximately 2:1 ratio of near-miss to no-harm MEs. The observed patterns of MEs reported, including type and timing, represent a target for further education. CLINICAL RELEVANCE: These results quantify the ME rate in general practice veterinary clinics, providing an initial benchmark for MEs during the perianesthetic period.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".