Is autoimmunology a discipline of its own? A big data-based bibliometric and scientometric analyses
Bibliographic record
Abstract
Autoimmunology is a super-specialty of immunology specifically dealing with autoimmune disorders. To assess the extant literature concerning autoimmune disorders, bibliometric and scientometric analyses (namely, research topics/keywords co-occurrence, journal co-citation, citations, and scientific output trends - both crude and normalized, authors network, leading authors, countries, and organizations analysis) were carried out using open-source software, namely, VOSviewer and SciCurve. A corpus of 169,519 articles containing the keyword "autoimmunity" was utilized, selecting PubMed/MEDLINE as bibliographic thesaurus. Journals specifically devoted to autoimmune disorders were six and covered approximately 4.15% of the entire scientific production. Compared with all the corpus (from 1946 on), these specialized journals have been established relatively few decades ago. Top countries were the United States, Japan, Germany, United Kingdom, Italy, China, France, Canada, Australia, and Israel. Trending topics are represented by the role of microRNAs (miRNAs) in the ethiopathogenesis of autoimmune disorders, contributions of genetics and of epigenetic modifications, role of vitamins, management during pregnancy and the impact of gender. New subsets of immune cells have been extensively investigated, with a focus on interleukin production and release and on Th17 cells. Autoimmunology is emerging as a new discipline within immunology, with its own bibliometric properties, an identified scientific community and specifically devoted journals.
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How this classification was reachedexpand
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.043 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".