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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".