Bibliographic record
Abstract
BACKGROUND: This study examined whether hypothermia (< 36.0°C) incidence among critically ill patients varied over time, the determinants of change, and the associated risk for ICU mortality. METHODS: Interrupted time series analysis among adults admitted to ICUs in Calgary, Canada over 8.5 years. Changes in the incidence of hypothermia within the first 24 hours of ICU admission were modelled using segmented regression. RESULTS: Among 15,291 first admissions to ICU, hypothermia incidence decreased from 29% to 21% during the study period. Implementation of a new temporal artery thermometer (TAT) was associated with the majority of the decrease in incidence (10%; 95% CI 7.1-13%; P < .0001). However, subgroup analysis revealed important differences between medical and surgical patients. Hypothermia incidence decreased among surgical patients before TAT implementation (0.4% per quarter, 95% CI 0.1-0.7%, P = .009), but not after, whereas in medical patients, the incidence increased after (1.0% per quarter, 95% CI 0.6-1.4%, P < .0001) but not before TAT implementation. Segmented logistic regression suggested that increases in the proportion of patients with non-traumatic neurologic admission diagnoses were associated with hypothermia incidence among medical patients, whereas there was no measurable clinical factor associated with the observed time trends among surgical patients. Hypothermia at ICU admission was independently associated with ICU mortality in medical and surgical patients throughout the entire study. CONCLUSION: The incidence of hypothermia at ICU admission was dependent on medical versus surgical status, and the method of non-invasive temperature measurement, but was persistently associated with ICU mortality.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".