Management of Heat-Related Illness and Injury in the ICU: A Concise Definitive Review
Bibliographic record
Abstract
OBJECTIVES: The increasing frequency of extreme heat events has led to a growing number of heat-related injuries and illnesses in ICUs. The objective of this review was to summarize and critically appraise evidence for the management of heat-related illnesses and injuries for critical care multiprofessionals. DATA SOURCES: Ovid Medline, Embase, Cochrane Clinical Trials Register, Cumulative Index to Nursing and Allied Health Literature, and ClinicalTrials.gov databases were searched from inception through August 2023 for studies reporting on heat-related injury and illness in the setting of the ICU. STUDY SELECTION: English-language systematic reviews, narrative reviews, meta-analyses, randomized clinical trials, and observational studies were prioritized for review. Bibliographies from retrieved articles were scanned for articles that may have been missed. DATA EXTRACTION: Data regarding study methodology, patient population, management strategy, and clinical outcomes were qualitatively assessed. DATA SYNTHESIS: Several risk factors and prognostic indicators for patients diagnosed with heat-related illness and injury have been identified and reported in the literature. Effective management of these patients has included various cooling methods and fluid replenishment. Drug therapy is not effective. Multiple organ dysfunction, neurologic injury, and disseminated intravascular coagulation are common complications of heat stroke and must be managed accordingly. Burn injury from contact with hot surfaces or pavement can occur, requiring careful evaluation and possible excision and grafting in severe cases. CONCLUSIONS: The prevalence of heat-related illness and injury is increasing, and rapid initiation of appropriate therapies is necessary to optimize outcomes. Additional research is needed to identify effective methods and strategies to achieve rapid cooling, the role of immunomodulators and anticoagulant medications, the use of biomarkers to identify organ failure, and the role of artificial intelligence and precision medicine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".