Hot spots and trends in inadvertent perioperative hypothermia: a bibliometric and visualized study
Bibliographic record
Abstract
Abstract Purpose: Inadvertent perioperative hypothermia (IPH) is a common complication of anesthesia and surgical exposure. Although considerably increased attention has been paid to the role of IPH over the past decades, a systematical bibliometric analysis on this topic has not yet been performed. This study aimed to investigate current research hotspot and predict future trends in IPH research using bibliometric analysis. Methods: The relevant literatures published from 2000 to 2022 were identified and selected from the Science Citation Index Expanded of Web of Science Core Collection (WoSCC). The VOSviewer and CiteSpace software were used to perform collaboration network analysis, co-citation analysis, co-occurrence analysis, and citation burst detection. Results: 1685 publications (1450 articles and 235 reviews) from WoSCC were used for analysis and visualization. The United States has made the largest contribution in this field, with most publications (535, 31.8%), and closely collaborations with China and Canada. The most productive institution and scholar in this field were University of Sao Paulo (30, 1.8%) and Professor Braeuer (19, 1.13%), respectively. Anesthesia and Analgesia was the most productive journal. The top ten keywords based on the co-occurrence analysis are “hypothermia”, “cardiopulmonary bypass”, “body temperature, “anesthesia”, “surgery”, “cardiac surgery”, “damage control surgery”, “perioperative hypothermia”, “trauma”, “bleeding”. The emerging research hotspot might be “active warming “, “prewarming”, and “forced-air warming”. Conclusion: This study analyzed the IPH using bibliometric and visual analysis. These results provide an instructive perspective on the current research and future directions and give a potential foundation for further research and clinical applications.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.063 | 0.034 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".