Bibliometric Analysis of the Top-100 Cited Articles on Postoperative Sleep During the Last 10 Years
Bibliographic record
Abstract
Background The quality of postoperative sleep is vital for surgical patients. A large number of patients after surgery suffer from sleep disorders. There are plenty of studies on postoperative sleep disorders. The aim of this study is to do a bibliometric analysis of the top-100 cited articles on postoperative sleep during the last 10 years, providing some clues to the investigators. Methods Publication retrieval was conducted in Web of Science (WoS) Core Collection on 12 January 2024. The 100 most frequently cited articles on postoperative sleep were identified and analyzed by VOSviewer and Excel. We mainly analyzed the publication year, citations, usage count, author, institution, country/region, journal and keywords. Results The number of citations ranged from 20 to 124 in WoS Core Collection, with a median of 35 and a mean of 40.79. USA (n = 39), China (n = 22) and Canada (n=9) ranked top three in terms of the number of publications and citations. Univ Copenhagen, Univ Toronto, and Lundbeck Ctr Fast Track Hip & Knee Arthroplasty were the top three institutions leading the researches on postoperative sleep. The journals specialized in Anesthesiology recorded the most high-quality articles. Postoperative pain, sleep, sleep quality, quality of life and postoperative delirium were the highly used keywords, while general anesthesia, fatigue, cognitive impairment and postoperative cognitive dysfunction were the latest topics. Conclusion At present, postoperative sleep researches have focused on the impacts of postoperative sleep disorders and pharmacological therapies to postoperative sleep disorders. However, non-pharmacological management of postoperative sleep should be paid more attention in the future.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.173 | 0.184 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".