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. In Audit 1 data were collected to determine the incidence and duration of perioperative hypothermia (defined as rectal temperatures <37·0°C). The results from Audit 1 were used to reach consensus on changes to be implemented to improve temperature management, including re-defining hypothermia as rectal temperature <37·5°C. Audit 2 was performed after 1 month with changes in place. RESULTS: Audit 1 revealed a high incidence of post-operative hypothermia (88·0%) and prolonged time periods (7·5 hours) to reach normothermia. Consensus changes were to use a forced air warmer on all dogs and measure rectal temperatures hourly post-operatively until temperature ≥37·5°C. After 1 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. The incidence of hypothermia at tracheal 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 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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".