Enhancing global clinical trial transparency for better health outcomes for all
Bibliographic record
Abstract
Background In 2022, WHO’s World Health Assembly adopted resolution WHA75.8, emphasizing the critical role of clinical trials in generating high-quality evidence and promoting equitable access to health interventions globally. In response, rapid landscape reviews were conducted to assess global clinical trial regulations, capacities, and funding distribution. Methods The analysis synthesized regulatory frameworks from 94 countries, institutional capacity data from the WHO International Clinical Trial Registry Platform (ICTRP), and funding data from World RePORT for trials registered between 2018-2022. Gaps in data availability and quality were assessed. Results Most countries reference international ethical guidelines, with universal requirements for ethics approval and informed consent. However, only 66% mandate public trial registration, and 40% require results reporting, with stark disparities between high- and low-income countries. High-income countries host over half of global trials; low-income countries contribute less than 1% despite high disease burden. Clinical trials sponsored by non-commercial entities are particularly scarce in low- and middle-income countries. Funding remains concentrated in the Americas and European regions, primarily driven by major funders such as the National Institutes of Health in the United States of America and European Commission. Significant data accessibility challenges persist due to incomplete registry records, inconsistent standards, lack of harmonized identifiers, and limited bulk data access. Recommendations Urgent actions include reinforcing international standards for trial registries, harmonizing data fields, improving registry interoperability, leveraging unique identifiers, enhancing multilingual accessibility, auditing data quality, pooling analytical resources, promoting open data policies, and investing in registry infrastructure and trained personnel. Conclusion Addressing data gaps and inequities in clinical trial ecosystems requires concerted action by global stakeholders. Improved data transparency and interoperability are essential to guide equitable research investments, foster coordination, and strengthen clinical trial capacity worldwide.
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 | 0.658 | 0.729 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.027 | 0.023 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".