A clinical audit cycle of post‐operative hypothermia in dogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.012 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it