Occurrence and outcome of fever in critically ill adults*
Bibliographic record
Abstract
OBJECTIVE: Although fever is common in the critically ill, only a small number of studies have specifically investigated its epidemiology in the intensive care unit (ICU). The objective of this study was to describe the occurrence of fever in the critically ill and assess its effect on ICU outcome. DESIGN: Retrospective cohort. Fever was defined by temperature > or = 38.3 degrees C and high fever by > or = 39.5 degrees C. SETTING: Calgary Health Region during 2000-2006. PATIENTS: All adults (> or = 18 yrs) admitted to ICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 24,204 ICU admission episodes occurred among 20,466 patients; 35% were classified as medical, 33% as cardiac surgical, 16% as other surgical, and 15% as trauma/neurologic. The cumulative incidence of fever and high fever was 44% and 8% and the incidence density was 24.3 and 2.7 per 100 days of ICU admission, respectively. The incidence density of fever was higher in trauma/neuro patients, males, younger patients, and was lower in those with admission Acute Physiology and Chronic Health Evaluation II scores > or = 25. Seventeen percent and 31% of patients with fever and high fever had associated positive cultures. Resolution of fever and high fever occurred in 27% and 53% of patients before ICU discharge and prolonged fever and high fever lasting for 5 or more days in the ICU occurred in 18% and 11% of febrile patients, respectively. Although the presence of fever was not associated with increased ICU mortality (13% vs. 12%; p = .08), high fever was associated with significantly increased risk for death (20.3% vs. 12%, p < .0001). After controlling for confounding factors using multivariable logistic regression models, the influence of fever on the ICU mortality varied significantly according to its timing of onset, degree, and main admission category. CONCLUSIONS: Fever is common in patients admitted to the ICU and its occurrence and impact on outcome varies among defined patient populations.
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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 | 0.001 | 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".