Prehospital interventions to prevent hypothermia in trauma patients: a scoping review
Bibliographic record
Abstract
Objective: The aim of this review is to map the prehospital rewarming measures used to prevent hypothermia among trauma victims. Background: Hypothermia is responsible for an increase of the mortality and morbidity in trauma victims and its recognition and early treatment are crucial for the victim’s haemodynamic stabilisation. Prehospital interventions are particularly important, especially those that target bleeding control, haemodynamic stability, and safe body temperature. Registered nurses may be pivotal to prevention and minimisation of the dangerous effects of hypothermia. Study design and methods: A scoping review was used to identify articles from several online databases from 2010 to 2018. Studies in English, Spanish, and Portuguese were included. Two reviewers performed data extractions independently. Results: Seven studies were considered eligible for this review: two quantitative research studies, one qualitative research study, and four literature reviews. Rewarming measures can be divided into two main groups: passive rewarming, which includes the use of blankets, positioning the response unit to act as a windbreak, removing the patients’ wet clothes, drying the patient’s body, and increasing the ambient temperature; and active rewarming which includes the use of heating pads, heated oxygen, warmed intravenous fluids, peritoneal irrigation, arteriovenous rewarming, and haemodialysis. Discussion: Active measures reported by the included studies were always used as a complement to the passive measures. Active rewarming produced an increase in core temperature, and passive rewarming was responsible for intrinsic heat-generating mechanisms that will counteract heat loss. Patients receiving passive warming in addition to active warming measures presented a statistically significant increase in body core temperature as well as an improvement in the discomfort caused by cold. Conclusion: Rewarming measures seem to be essential for the prevention of hypothermia and to minimise the discomfort felt by the patient. In many countries registered nurses can play important roles in the prehospital context of trauma victim’s assistance. Greater understanding of these roles is necessary to the development of better practices.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".