A clinical audit cycle of post-operative hypothermia in dogs
Bibliographic record
Abstract
Objectives: Use of clinical audits to assess and improve perioperative hypothermia management in client-owned dogs. Methods: Two clinical audits were performed. Audit 1: data were collected to determine the incidence and duration of perioperative hypothermia (defined rectal temperatures < 37.5˚C). The results from Audit 1 were presented to clinic staff and a consensus reached on changes to be implemented to improve temperature management. Following one month with the changes in place, Audit 2 was performed to assess performance. Results: Audit 1 revealed a high incidence of post-operative hypothermia (88.9%) and prolonged time periods for animals to reach normothermia. Following discussion, a consensus was reached to: 1. measure rectal temperatures hourly post-operatively until a temperature ≥ 37.5˚C was achieved and 2. use a forced air warmer on all dogs until rectal temperature was ≥ 37˚5. After one month with the implemented changes, Audit 2 identified a significant reduction in the time to achieve a rectal temperature of ≥ 37.5˚C, with 75% of dogs achieving this goal by 3.5 hours (7.5 hours for Audit 1, p = 0.01). The incidence of hypothermia at extubation remained high in Audit 2 (97.3% with a rectal temperature < 37.5˚C). Clinical significance: Post-operative hypothermia was improved through simple changes in practice, showing that clinical audit is a useful tool for monitoring post-operative hypothermia and improving patient care. Overall management of perioperative hypothermia could be further improved with earlier intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".