A prospective survey of veterinary anesthesia equipment in Alberta, Canada, using a standardized checkout procedure.
Bibliographic record
Abstract
Background: In both human and veterinary medicine, it is recommended that an anesthetic machine checkout procedure (preuse check) be performed daily, with some items tested before each case, to confirm safe function and the check results recorded. Objective: The objective of this prospective study was to evaluate anesthetic machines in private veterinary clinics in Alberta (Canada) using a standardized checkout procedure. Animals and procedures: One-hundred consecutive anesthetic machines were assessed. For each item of the checkout procedure, a "pass," "fail," or "not applicable" score was awarded. "Not applicable" indicated an item that could not be evaluated. Results: Few machines (10%) evaluated had a secondary oxygen supply, no machines had an oxygen supply pressure alarm, and leaks were identified in 31 and 17% of rebreathing and non-rebreathing systems, respectively. Thirty-nine percent of machines did not have a high-pressure circuit alarm, 86% of machines were attached to an active scavenging system, although it was improperly connected in 56% of cases, and only 2% of machines were accompanied by a checkout log. Conclusion and clinical relevance: There was widespread variation in anesthetic machine standards and function, highlighting the value of performing a regular machine checkout procedure in creating a foundation for safe anesthetic practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".