Bibliometric profile of the global scientific research on multiple sclerosis (2003–2012)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aim of this paper is to analyse the scientific research on multiple sclerosis using a bibliographic analysis of articles published during the period 2003-2012. METHODS: The items under study were obtained from the Science Citation Index-Expanded (SCI-E) database, which was accessed through the Web of Science (WOS) platform. All records with the term 'multiple sclerosis' in the title, plus all articles published in the journals Multiple Sclerosis and Multiple Sclerosis Journal, were analysed. RESULTS: A total of 9778 articles, with 160,966 citations, were retrieved on multiple sclerosis, and the majority of the articles were published in Multiple Sclerosis Journal (n = 1511). The articles were published in journals belonging to 135 different subject areas, with the greatest number of papers falling under the category of clinical neurology. The countries that published the largest numbers of articles were the United States (US) (n = 2786), Italy (n = 1263), the United Kingdom (n = 1147) and Germany (n = 1018). International collaborations produced 20.4% of the papers. CONCLUSIONS: We emphasise the progressive growth of publications worldwide, the publication of articles in a wide variety of journals covering numerous subject areas, and the research leadership of Western countries, most notably European countries, the US and Canada.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.083 | 0.141 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".