Clinical trial trends over the last 5 years among the BRICS (Brazil, Russia, India, China, and South Africa) nations
Bibliographic record
Abstract
Purpose: Although the Americas and Europe have historically dominated the global research landscape, emerging economies - Brazil, Russia, India, China, and South Africa (BRICS) have significantly increased their contributions in recent years. This article studies clinical trial trends in the BRICS nations between 2018 and 2022 and compares it with trends in the G7 nations (comprising Canada, France, Germany, Italy, Japan, the UK, the USA, and the European Union). This will help stakeholders in planning drug development strategies. Materials and Methods: Data were collected from the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) and the World Bank database. An electronic search was done for the total number of trials registered between January 1, 2018, and March 15, 2023. Information was analyzed based on the year of registration, therapeutic area, type of intervention, sponsorship, and type of special population. The trial density indices (TDIs) were calculated based on population (Xi) and gross domestic product (GDP) (Yi) using author-derived formulae. Results: Altogether 2, 77, 536 trials from the BRICS and G7 were registered. China and the US had the most trials among the BRICS and G7, respectively. Between 2018 and 2022, the gap between the BRICS and G7 steadily reduced. The most common indication for clinical trials among the BRICS was cancer. Based on population, the TDI was the highest in China and the lowest in Russia. In proportion to the GDP, the TDI was maximum in Russia and minimum in India. Conclusion: There is a remarkable reduction in the gap in clinical trial trends between the BRICS and G7 nations. Among the BRICS, India and China are at the forefront in drug development. There is scope for improvement in trial density based on India's population and GDP. Stakeholders are likely to utilize the strengths of the BRICS as an attractive destination for investment in this area.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".