Publications of systematic review and meta-analysis in the indexed anesthesia journals: a 10-year bibliometric analysis
Bibliographic record
Abstract
Background: Anesthesiology research is growing at a rapid pace. It is essential to understand the scope and trends over time to identify gaps and future areas for growth. Systematic reviews and meta-analyses (SRMA) are summaries of the best available evidence to address a specific research question via a comprehensive literature search, in-depth analyses, and synthesis of results. High-quality SRMA are increasingly used and play an essential role in medical research. Objective: We aimed to explore the trends of SRMA in indexed anesthesia journals. Methods: SRMA published in indexed anesthesia journals from 2013 to 2023 were retrieved from the Web of Science database. Data were presented via descriptive statistics. We used CiteSpace 6.1.R6 to analyze countries, institutions, journals, authors, and keywords through visual maps to explore the research hotspots and trends. The journal's Journal Citation Reports partition, impact factor, annual publications, journals H-index, and a number of highly-cited papers were calculated in the WoS database. Results: A total of 34 indexed anesthesia journals and 3,004 SRMA were included. The year 2021 was the year with the most SRMA (385/3,004). Out of the 3,004 SRMAs, 36 (0.03%) were highly cited papers, and 22 of the 36 highly cited papers focused on "pain management." BRITISH JOURNAL OF ANAESTHESIA had the highest 5-year impact factor (9.6) in 2022 Journal Citation Reports, the most significant number of publications (268/3,004), the highest total number of citations (13,173/86,145), and the most significant number of SRMAs cited more than 100 (36/160). ANAESTHESIA achieved the highest impact factor in the 2022 Journal Citation Reports (10.7) and the highest average annual citations (58.82). PAIN had the highest number of highly cited papers (15/36). The United States of America was the most productive country, with 823/3,004 SRMAs. University Toronto had the highest number of publications (245/3,004). The most frequent of keywords was the topic "Pain Management" (1,622/29.1%). Conclusion: This present study would be valuable to practitioners, academics, researchers, and students in understanding the dynamics of progress in anesthesiology.
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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 | 0.071 | 0.266 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.275 | 0.282 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".