International Collaboration and Commercial Involvement in Randomized Controlled Trials From 10 Leading Countries, 1997 Through 2019
Bibliographic record
Abstract
Randomized controlled trials (RCTs) affect clinical decisions and their number is increasing. However, trends in international collaboration on RCTs and involvement of healthcare-related industries, the latter of which may contribute to bias, are not known. The objectives were to identify concerns surrounding RCTs, and to quantify changes in (1) the numbers of RCT articles in journals of high clinical importance, (2) international collaboration, and (3) commercial involvement in RCTs by authors in countries that contribute the most to the scientific literature. This was not a systematic review of the medical literature. It is a descriptive study of trends during the past two decades. We extracted RCT articles from MEDLINE data (1997-2019). When grouped by authors' country, the analyses were limited to the 10 leading countries in the natural sciences, as defined by the Nature Index 2019 Annual Tables. The Core Clinical Journals (CCJ) filter in PubMed was used to identify journals that were likely to be highly relevant to clinical practice. RCT articles that included authors from multiple countries were used as examples of international collaboration, and RCTs in which at least one author's affiliation was corporate were considered to have commercial involvement. The annual number of RCT articles more than doubled (from 10,360 to 22,384), but the number published in the CCJ was essentially unchanged (from 2,245 to 2,346). The vast majority of RCT articles had US-based authors. International collaboration increased in nine of the 10 countries studied, and it was particularly common among researchers in Europe, Canada, and Australia. In contrast, international collaboration decreased in China. Regarding commercial involvement, between 1997 and 2019 the proportion of single-country RCTs with commercial involvement decreased (from 12.4% to 3.8% for the United States, and from 2.5% to 0.0% for Europe-Canada-Australia). In contrast, the proportion of international-collaborative RCTs with commercial involvement increased (from 9.2% to 17.6% for the United States, and from 17.9% to 21.3% for Europe-Canada-Australia). The largest change in commercial involvement was the 12-fold increase in Japan: from 3% to 36% (1997-2019). Japan was also noteworthy for its 28-percentage-point decrease in first-authorship of RCT articles from 2012 to 2019. In conclusion, recent increases in the number of RCT articles have occurred almost exclusively outside the CCJ. Thus, many newer RCT articles might have relatively low clinical relevance or impact. International collaboration has generally increased, along with commercial involvement. The latter has become particularly common in Japan, increasing the potential for sponsorship bias. The effects of ongoing attempts to reverse that trend should be evaluated.
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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 | 0.339 | 0.657 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.086 | 0.125 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier 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".