Systematic scoping review of cluster randomised trials conducted exclusively in low-income and middle-income countries between 2017 and 2022
Bibliographic record
Abstract
OBJECTIVE: Cluster randomised trials (CRTs) are used for evaluating health-related interventions in low-income and middle-income countries (LMICs) but raise complex ethical issues. To inform the development of future ethics guidance, we aim to characterise CRTs conducted exclusively in LMICs by examining the types of clusters, settings, author affiliations and primary clinical focus and to evaluate adherence to trial registration and ethics reporting requirements over time. DESIGN: A systematic scoping review using the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews. DATA SOURCES: We searched MEDLINE between 1 January 2017 and 17 August 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included primary reports of CRTs evaluating health-related interventions, conducted exclusively in LMICs and published in English between 2017 and 2022. DATA EXTRACTION AND SYNTHESIS: Data were extracted by one reviewer; a second reviewer verified accuracy by extracting data from 10% of the reports. Results were summarised overall and categorised by country's economic level or publication year. RESULTS: Among 800 identified CRTs, 400 (50.0%) randomised geographical areas and 373 (46.6%) were conducted in Africa. 30 (3.7%) had no authors with an LMIC affiliation, and 246 (30.8%) had neither first nor last author with an LMIC affiliation. The relative frequency of first or last authors holding an LMIC affiliation increases as a country's economic level increases. Most CRTs focused on reducing maternal and neonatal disorders (106, 13.3%). 670 (83.8%) CRTs reported trial registration, 786 (98.2%) reported research ethics committee review and 757 (94.6%) reported consent statements. Among the 757 CRTs, 46 (6.1%) reported a waiver or no consent and, among these, 10 (21.7%) did not provide a rationale. Gatekeepers were identified in 403 (50.4%) CRTs. No meaningful trends were observed in adherence to trial registration or ethics reporting requirements over time. CONCLUSION: Our findings suggest existing inequity in authorship practices. There is high adherence to trial registration and ethics reporting requirements, although greater attention to reporting a justification for using a waiver of consent is needed.
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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 | 0.108 | 0.363 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.013 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".