A systematic review on the effectiveness of prewarming to prevent perioperative hypothermia
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To analyse available research on the effectiveness of prewarming to prevent perioperative hypothermia and identify knowledge gaps for future research. BACKGROUND: Perioperative hypothermia is common and causes complications, such as coagulation and platelet function abnormalities; increased cardiac morbidity, surgical site infection, and pressure ulcer incidence levels. In this context, several methods have been investigated to prevent perioperative hypothermia, including prewarming. Prewarming is defined as the warming of peripheral tissues or the skin surface before anaesthetic induction and may consist of an active cutaneous warming system or the preoperative administration of vasodilation drugs. DESIGN: Systematic review. METHODS: We searched CINAHL, EMBASE, Cochrane Register of Controlled Trials and Medline (January 1990-November 2011) for randomised controlled trials on the effectiveness of prewarming for prevention of perioperative hypothermia, published in English, Spanish and Portuguese, and involving elective surgery patients aged 18 years or older. RESULTS: Of 730 identified studies, only 13 met the inclusion criteria. After hand-searching the reference lists of included studies, an additional study was identified for a total sample of 14 studies. The results suggest that forced-air warming system is effective to reduce hypothermia when applied for the prewarming of surgical patients. CONCLUSION: Prewarming patients with the forced-air warming system might be effective to reduce perioperative hypothermia, and new studies are needed to examine the use of carbon fibre technology. RELEVANCE TO CLINICAL PRACTICE: Nurses can use this review to inform decision-making on a prewarming programme in the perioperative period. They can also develop research on strategies to put in practice prewarming in the surgical context.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".