Pharmacotherapy of fever control among hospitalized adult patients
Bibliographic record
Abstract
INTRODUCTION: Fever is common and associated with increased mortality among hospitalized adults. This article will review the pharmacotherapy of commonly prescribed antipyretic drugs including the rationale for and against temperature control in febrile adults, as well as the evidence associated with fever control in specific patient populations. AREAS COVERED: Though fever is common, the molecular basis of pyrexia, and the interaction of these pathways with commonly prescribed antipyretic drugs are not fully understood. Furthermore, while experimental and clinical studies clearly demonstrate that pyrexia is harmful in select patients, available clinical trial data are unable to suggest an evidence-based approach to the treatment of fever. Interestingly, this also applies to patients with an acute neurologic injury wherein the treatment of fever with antipyretic therapy has become a common management strategy. EXPERT OPINION: Few adequately powered clinical trials have investigated temperature control strategies in febrile patients. Therefore, it is not possible to define an evidence-based approach to the control of fever in hospitalized adults. Further clinical and translational research is required to identify the patients most likely to benefit from a strategy of fever control versus permissive hyperthermia, and to determine the antipyretic therapies associated with the greatest improvement in outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".