Thyroid disorders in children and adolescents
Bibliographic record
Abstract
Background: Several countries research thyroid problems in children and adolescents. However, a scientometric assessment of global research in this field is unavailable. Aim: We aimed to provide a comprehensive assessment of research in thyroid disorders in children during 1990–2019. Methods: The data on pediatric thyroid disorders (PTDs) publications were retrieved from the Scopus database and analyzed using select bibliometric tools. Results: There were 4658 publications over the 30-year period registering an average annual and 15-year cumulative growth of 6.9% and 149.4%, respectively, and averaging 24.0 citations per paper. Of the 144 participating countries, the top ten contributed 69.9% of the global share. The most productive countries were the USA, Italy, and UK, whereas Netherlands, Canada, and the USA were the most impactful. Of the 745 participating organizations and 1275 authors, the top 20 of each contributed 26.2% and 7.9% of publication share, and 42.8% and 14.6% of citation share, respectively. The top three most productive organizations were INSERM, France, National Institute of Health, USA, and National Cancer Research Institute, USA, whereas the top three most productive authors were S. Yamashita, L. Persani, and G. Weber. Journal of Clinical Endocrinology and Metabolism, Journal of Pediatric Endocrinology and Metabolism, and Thyroid were the journals that published most research in PTDs. Conclusions: There is a substantial recent increase in the quantity of research on PTDs dominated by the North-American and Western-European countries. The vast disparities in pediatric thyroid research between high- and low-income countries need to be addressed through collaborations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".