Risk factors for postoperative hypothermia in non-cardiac surgery patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Postoperative hypothermia seems to be a common problem in surgical patients but is easily ignored. This study aimed to identify risk factors for postoperative hypothermia in non-cardiac surgery patients. METHODS: We searched databases including PubMed, Embase, Web of Science, Cochrane Library, CINAHL, VIP, Wan Fang, CNKI, and CBM from inception to April 2025. The studies were selected using inclusion and exclusion criteria. Two reviewers screened studies, extracted data, and independently evaluated the risk of bias. The quality of the study was assessed with the Newcastle-Ottawa Scale, and a meta-analysis was carried out with Revman 5.4 software. RESULTS: (OR = 1.83), ASA III-IV (OR = 1.87), endoscopic surgery (OR = 1.93), intraoperative blood loss ≥ 100ml (OR = 2.35), intravenous fluid ≥ 1000ml (OR = 1.87), blood transfusion (OR = 1.80), duration of anesthesia > 1 h (OR = 1.99) and duration of surgery > 1 h (OR = 2.34) were significant risk factors that contributed to postoperative hypothermia in non-cardiac surgery patients. CONCLUSION: There are many risk factors for postoperative hypothermia in patients undergoing non-cardiac surgery. The results of this research may improve clinician awareness, risk stratification, and prevention of postoperative hypothermia in non-cardiac surgery patients.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".