Risk factors for intraoperative hypothermia during laparoscopic surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To systematically evaluate the risk factors for intraoperative hypothermia in patients undergoing laparoscopic surgery globally; and to provide information on how to prevent complications and, should they occur, how to intervene. METHODS: This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO; No. CRD42024555506). We searched the following databases: PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Web of Science Core Collection, EMBASE, China National Knowledge Infrastructure (CNKI), and Wanfang. Data on risk factors for hypothermia during laparoscopic surgery were systematically collected through June 1, 2024. After evaluating references that met Newcastle-Ottawa scale (NOS) or Agency for Healthcare Research and Quality (AHRQ) inclusion criteria, we performed a meta-analysis of the extracted data using RevMan version 5.4. RESULTS: We included 11 studies with a cumulative sample size of 3550 cases and extracted 14 risk factors. Meta-analysis results showed that age (odds ratio [OR], 1.02; 95% confidence interval [CI], 1.00-1.03; P = 0.04), total amount of intraoperative CO2 injected into the abdominal cavity > 200 L (OR, 1.5; 95% CI, 1.30-1.81; P < 0.001), duration of operation > 120 min (OR, 2.32; 95% CI, 2.03-2.65; P < 0.001), duration of anesthesia > 150 min (OR, 1.55; 95% CI, 1.26-1.92; P < 0.001), intravenous (IV)-fluid volume>1500 mL (OR = 1.77; 95% CI, 1.48-2.12; P < 0.001), and intraoperative blood loss ≥ 150 mL (OR, 1.66; 95% CI, 1.27-2.17; P < 0.001) were risk factors for intraoperative hypothermia. CONCLUSIONS: We found that age, total amount of CO2 injected into the abdominal cavity during the operation, operation duration, anesthesia duration, IV-fluid volume, and intraoperative blood loss to be risk factors for intraoperative hypothermia in patients undergoing laparoscopic surgery. Given the limitations of the available literature's quantity and quality, our conclusions should be verified by higher-quality studies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".