Table 1_Publications of systematic review and meta-analysis in the indexed anesthesia journals: a 10-year bibliometric analysis.docx
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.009 |
| Bibliometrics | 0.458 | 0.884 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.759 | 0.004 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".