Perianesthetic Dose Calculation Errors by Veterinary Students During a Live Animal Teaching Laboratory
Bibliographic record
Abstract
Performing drug dose calculations is an expected fundamental skill in veterinary medicine. Calculation errors are a significant contributor to medication errors in veterinary anesthesia and have the potential to harm patients. Investigating dose calculation errors in a clinical environment with live patients has not been reported in veterinary medicine. Identifying and reporting calculation errors can assist with teaching and mitigating future dose calculation errors. In a prospective, observational study, drug dose calculations included in the proposed anesthesia protocols of 53 third-year veterinary students for a canine and feline spay/neuter laboratory were reviewed. Calculation error incidence, type, and drugs involved were analyzed. A total of 686 drug doses were calculated for 83 patients. Twelve dose calculation errors were identified in nine anesthesia protocols, representing a protocol error rate of 10.8% (9/83) and an overall drug dose calculation error rate of 1.8% (12/686). The majority of errors (83.3%; 10/12) would have led to overdoses, whereas two errors (16.7%; 2/12) would have resulted in underdoses. Drug dose calculation errors are common during anesthetic planning by veterinary students. The occurrence of calculation errors poses a risk to patient safety, highlighting the need for effective teaching and training in this skill, as well as the role of error-reducing strategies such as independent double-checking of calculations.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".