A Bibliometric Analysis of Anemia Research in Children or Adolescents in the Last 10 Years
Bibliographic record
Abstract
INTRODUCTION: Both nationally and internationally, anemia is one of the greatest public health challenges. It mainly affects children, adolescents, and women of reproductive age and manifests itself in different etiological forms. To examine the worldwide scientific production on anemia in children and adolescents focusing on the Scopus database, in a period between 2011 and 2020, the present bibliometric study is proposed. METHODOLOGY: The Scopus database was used as the main data source to collect relevant manuscripts on anemia in children and adolescents from 2011 to 2020. The CSV data were exported to SciVal for analysis of most published topics, collaborations, most published institutions, productivity by journal category, most published journals, most published medical specialties, and most published authors. RESULTS: Of the 1784 manuscripts reported, it was shown that the year 2020 had the highest number of publications with 33, 19, 13, and 15 articles in the Q1 (top 25%), Q2 (top 26%–50%), Q3 (top 51%–75%), and Q4 quartiles (76%–100%), respectively. The University of Pennsylvania (USA), Johns Hopkins University (USA), and Baylor College of Medicine (USA) were the top three institutions with the highest article production. The top three places were for Pediatric Blood and Cancer, Public Health Nutrition, and Journal of Pediatric Hematology/Oncology with 20, 12, and 11 publications, respectively. CONCLUSIONS: In recent years, there has been evidence of an increase in the number of publications referring to anemia in children and adolescents, experiencing a notorious increase from 2015. In terms of scientific production, the United States, Egypt, and Canada are positioned as the leading countries in this field.
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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 | Other design | high |
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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.175 | 0.225 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".