Trends and Emerging Research Areas in Postoperative Sleep Disturbances: A Bibliometric Analysis
Bibliographic record
Abstract
Purpose: Postoperative sleep disturbance (PSD) is highly prevalent and significantly affects patient prognosis. Studies on PSD have received increasing attention, resulting in a surge in related publications. However, comprehensive analyses that can objectively reflect changes in scientific knowledge and identify the latest research trends in this field are lacking. Methods: Articles and reviews focusing on PSD were extracted from the Web of Science Core Collection database. Bibliometrix, VOSviewer, and CiteSpace were used to conduct bibliometric analysis and map the visualization network. Results: A total of 1,559 publications were extracted from the database, including 1,370 articles and 189 reviews. There has been a consistent increase in the number of publications, with an average annual growth rate of 16.56%, led by the United States in terms of research output. Notably, the University of Toronto was a prominent contributor. Co-cited reference network analysis revealed 17 well-structured networks (Q = 0.8174, S = 0.9441). Six major research trends were identified: mechanisms of sleep related to anesthesia, role of melatonin in sleep disturbances, pain management strategies, effects of analgesic drugs, impact of dexmedetomidine on sleep quality, and postoperative recovery. Keywords analysis highlighted the emerging roles of dexmedetomidine, neuroinflammation, and acupuncture. Conclusion: Bibliometric analysis provides a helpful summary of postoperative sleep disturbances that have changed over time, by identifying knowledge points and developing trends. Future research should focus on integrating multidisciplinary approaches, exploring neuroinflammation, evaluating non-pharmacological interventions and long-term outcomes, which will advance scientific knowledge, enhance clinical practice, and improve patient outcomes and quality of life.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.099 | 0.297 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".