Impact of Fever on Outcome in Patients With Stroke and Neurologic Injury
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Many studies associate fever with poor outcome in patients with neurological injury, but this relationship is blurred by divergence in populations and outcome measures. We sought to incorporate all recent scholarship addressing fever in brain-injured patients into a comprehensive meta-analysis to evaluate disparate clinical findings. METHODS: We conducted a Medline search for articles since January 1, 1995 (in English with abstracts, in humans) and hand searches of references in bibliographies and review articles. Search terms covered stroke, neurological injury, thermoregulation, fever, and cooling. A total of 1139 citations were identified; we retained 39 studies with 67 tested hypotheses contrasting outcomes of fever/higher body temperature and normothermia/lower body temperature in patients with neurological injury covering 14431 subjects. A separate meta-analysis was performed for each of 7 outcome measures. Significance was evaluated with Zc developed from probability values or t values. Correlational effect size, r (es), was calculated for each study and used to derive Cohen's d unbiased combined effect size and relative risk. RESULTS: Fever or higher body temperature was significantly associated with worse outcome in every measure studied. Relative risk of worse outcome with fever was: mortality, 1.5; Glasgow Outcome Scale, 1.3; Barthel Index, 1.9; modified Rankin Scale, 2.2; Canadian Stroke Scale, 1.4; intensive care length of stay, 2.8; and hospital length of stay, 3.2. CONCLUSIONS: In the pooled analyses covering 14431 patients with stroke and other brain injuries, fever is consistently associated with worse outcomes across multiple outcome measures.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".