Data flow within global clinical trials: a scoping review
Bibliographic record
Abstract
Objective To document clinical trial data flow in global clinical trials published in major journals between 2013 and 2021 from Global South to Global North. Design Scoping analysis Methods We performed a search in Cochrane Central Register of Controlled Trials (CENTRAL) to retrieve randomised clinical trials published between 2013 and 2021 from The BMJ, BMJ Global Health, the Journal of the American Medical Association, the Lancet, Lancet Global Health and the New England Journal of Medicine. Studies were included if they involved recruitment and author affiliation across different country income groupings using World Bank definitions. The direction of data flow was extracted with a data collection tool using sites of trial recruitment as the starting point and the location of authors conducting statistical analysis as the ending point. Results Of 1993 records initially retrieved, 517 studies underwent abstract screening, 348 studies underwent full-text screening and 305 studies were included. Funders from high-income countries were the sole funders of the majority (82%) of clinical trials that recruited across income groupings. In 224 (73.4%) of all assessable studies, data flowed exclusively to authors affiliated with high-income countries or to a majority of authors affiliated with high-income countries for statistical analysis. Only six (3.2%) studies demonstrated data flow to lower middle-income countries and upper middle-income countries for analysis, with only one with data flow to a lower middle-income country. Conclusions Global clinical trial data flow demonstrates a Global South to Global North trajectory. Policies should be re-examined to assess how data sharing across country income groupings can move towards a more equitable model.
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 | 0.583 | 0.836 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.071 | 0.068 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.010 | 0.010 |
| 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; 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".