Fever Is Associated with Delayed Ventilator Liberation in Acute Lung Injury
Bibliographic record
Abstract
BACKGROUND: Acute lung injury (ALI) is characterized by inflammation, leukocyte activation, neutrophil recruitment, endothelial dysfunction, and epithelial injury, which are all affected by fever. Fever is common in the intensive care unit, but the relationship between fever and outcomes in ALI has not yet been studied. We evaluated the association of temperature dysregulation with time to ventilator liberation, ventilator-free days, and in-hospital mortality. METHODS: Analysis of a prospective cohort study, which recruited consecutive patients with ALI from 13 intensive care units at four hospitals in Baltimore, Maryland. The relationship of fever and hypothermia with ventilator liberation was assessed with a Cox proportional hazards model. We evaluated the association of temperature during the first 3 days after ALI with ventilator-free days, using multivariable linear regression models, and the association with mortality was evaluated by robust Poisson regression. MEASUREMENTS AND MAIN RESULTS: Of 450 patients, only 12% were normothermic during the first 3 days after ALI onset. During the first week post-ALI, each additional day of fever resulted in a 33% reduction in the likelihood of successful ventilator liberation (95% confidence interval [CI] for adjusted hazard ratio, 0.57 to 0.78; P < 0.001). Hypothermia was independently associated with decreased ventilator-free days (hypothermia during each of the first 3 d: reduction of 5.58 d, 95% CI: -9.04 to -2.13; P = 0.002) and increased mortality (hypothermia during each of the first 3 d: relative risk, 1.68; 95% CI, 1.06 to 2.66; P = 0.03). CONCLUSIONS: Fever and hypothermia are associated with worse clinical outcomes in ALI, with fever being independently associated with delayed ventilator liberation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".