Global mapping of randomised trials related articles published in high-impact-factor medical journals: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Randomised controlled trials (RCTs) provide the most reliable information to inform clinical practice and patient care. We aimed to map global clinical research publication activity through RCT-related articles in high-impact-factor medical journals over the past five decades. METHODS: We conducted a cross-sectional analysis of articles published in the highest ranked medical journals with an impact factor > 10 (according to Journal Citation Reports published in 2017). We searched PubMed/MEDLINE (from inception to December 31, 2017) for all RCT-related articles (e.g. primary RCTs, secondary analyses and methodology papers) published in high-impact-factor medical journals. For each included article, raw metadata were abstracted from the Web of Science. A process of standardization was conducted to unify the different terms and grammatical variants and to remove typographical, transcription and/or indexing errors. Descriptive analyses were conducted (including the number of articles, citations, most prolific authors, countries, journals, funding sources and keywords). Network analyses of collaborations between countries and co-words are presented. RESULTS: We included 39,305 articles (for the period 1965-2017) published in forty journals. The Lancet (n = 3593; 9.1%), the Journal of Clinical Oncology (n = 3343; 8.5%) and The New England Journal of Medicine (n = 3275 articles; 8.3%) published the largest number of RCTs. A total of 154 countries were involved in the production of articles. The global productivity ranking was led by the United States (n = 18,393 articles), followed by the United Kingdom (n = 8028 articles), Canada (n = 4548 articles) and Germany (n = 4415 articles). Seventeen authors who had published 100 or more articles were identified; the most prolific authors were affiliated with Duke University (United States), Harvard University (United States) and McMaster University (Canada). The main funding institutions were the National Institutes of Health (United States), Hoffmann-La Roche (Switzerland), Pfizer (United States), Merck Sharp & Dohme (United States) and Novartis (Switzerland). The 100 most cited RCTs were published in nine journals, led by The New England Journal of Medicine (n = 78 articles), The Lancet (n = 9 articles) and JAMA (n = 7 articles). These landmark contributions focused on novel methodological approaches (e.g. the "Bland-Altman method") and trials on the management of chronic conditions (e.g. diabetes control, hormone replacement therapy in postmenopausal women, multiple therapies for diverse cancers, cardiovascular therapies such as lipid-lowering statins, antihypertensive medications, and antiplatelet and antithrombotic therapy). CONCLUSIONS: Our analysis identified authors, countries, funding institutions, landmark contributions and high-impact-factor medical journals publishing RCTs. Over the last 50 years, publication production in leading medical journals has increased, with Western countries leading in research but with low- and middle-income countries showing very limited representation.
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 | 0.044 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.068 | 0.069 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".