Veterinary students do not need an elephantine memory: Effectiveness of an anesthetic pre-induction checklist.
Bibliographic record
Abstract
Background: Checklists are widely recognized as safety measures in both aviation and human medicine, effectively preventing omissions caused by memory failures. Objective: To assess whether a pre-induction safety checklist completed by veterinary students during a spay/neuter laboratory minimized the number of incomplete pre-induction tasks. Participants and procedure: Third-year veterinary students (N = 53) managed the anesthesia of dogs and cats admitted for spay/neuter surgery under supervision. The use of a pre-induction checklist was mandatory to ensure appropriate preparation before anesthesia induction. Differences in checklist completeness between the 1st and 2nd wk of the spay/neuter laboratory were compared using Fisher's exact test. Results: = 0.0046). The most frequently missed item was premeasuring the endotracheal tube insertion depth (42.2%, 35/83), followed by failure to leak-test the endotracheal tube cuffs and not having gauze available (15.7%, 13/83 for each). Finally, the checklist identified closed adjustable pressure-limiting valves in 4.8% (4/83) of cases. Conclusion and clinical relevance: The pre-induction checklist was effective in ensuring that veterinary students completed relevant pre-induction tasks during a spay/neuter laboratory. Results suggested the pre-induction checklist was a valuable tool that improved patient safety and prevented life-threatening equipment errors such as closed adjustable pressure-limiting valves.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".