The pediatric anesthesiology publication activity and landscape over the past two decades: A longitudinal scientometric analysis
Bibliographic record
Abstract
BACKGROUND: Scientometric analyses characterize the output of research publications using quantitative methods. While it has been reported that the number of publications in anesthesiology has been increasing for years, the global research activity in pediatric anesthesiology and its landscape is largely unknown. AIMS: To examine the activity, developmental dynamics, and collaboration landscape of research publications in pediatric anesthesiology over the past two decades. METHODS: PubMed and WebOfScience were searched for pediatric anesthesiology publications published between 2001 and 2020. The identified publications were exported into a database, matched, curated, and then assigned to one or more countries according to their affiliation field(s). The primary outcome was the publication activity and its growth rate. Secondary outcomes included the geographical distribution, the evolution of international collaborations (as indicated by articles affiliated with more than one country), and the main sources. RESULTS: Thirty-four thousand, three hundred and forty-three pediatric anesthesiology publications were retrieved. The compound annual growth rate over the study period was +7.6%. The highest annual growth rate was +20.6% from 2019 to 2020. Corresponding authors were most often affiliated with USA (32.5%), Germany (5.5%), and China (5.5%). China (+22.9%), Iran (+21.7%), and India (+16.1%) had the highest compound annual growth rates. 6001 (17.5%) articles involved international collaboration, with a compound annual growth rate of +13.1%. The most frequent collaboration was between USA and Canada (716 articles together). The most prominent source was Pediatric Anesthesia (10.0%). CONCLUSIONS: Publication activity in pediatric anesthesiology has increased from 2001 to 2020 and has become more geographically diverse. With the volume of international collaborations even outpacing this growth, it is hoped that this will gradually lead to a larger evidence base in pediatric anesthesia.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".