Diagnosis and management of temperature abnormality in ICUs: a EUROBACT investigators' survey
Bibliographic record
Abstract
INTRODUCTION: Although fever and hypothermia are common abnormal physical signs observed in patients admitted to intensive care units (ICU), little data exist on their optimal management. The objective of this study was to describe contemporary practices and determinants of management of temperature abnormalities among patients admitted to ICUs. METHODS: Site leaders of the multi-national EUROBACT study were surveyed regarding diagnosis and management of temperature abnormalities among patients admitted to their ICUs. RESULTS: Of the 162 ICUs originally included in EUROBACT, responses were received from 139 (86%) centers in 23 countries in Europe (117), South America (8), Asia (5), North America (4), Australia (3) and Africa (2). A total of 117 (84%) respondents reported use of a specific temperature threshold in their ICU to define fever. A total of 14 different discrete levels were reported with a median of 38.2°C (inter-quartile range, IQR, 38.0°C to 38.5°C). The use of thermometers was protocolized in 91 (65%) ICUs and a wide range of methods were reportedly used, with axillary, tympanic and urinary bladder sites as the most common as primary modalities. Only 31 (22%) of respondents indicated that there was a formal written protocol for temperature control among febrile patients in their ICUs. In most or all cases practice was to control temperature, to use acetaminophen, and to perform a full septic workup in febrile patients and that this was usually directed by physician order. While reported practice was to treat nearly all patients with neurological impairment and most patients with acute coronary syndromes and infections, severe sepsis and septic shock, this was not the case for most patients with liver failure and fever. CONCLUSIONS: A wide range of definitions and management practices were reported regarding temperature abnormalities in the critically ill. Documenting temperature abnormality management practices, including variability in clinical care, is important to inform planning of future studies designed to optimize infection and temperature management strategies in the critically ill.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".