Systematic review and pooled analysis of randomized controlled trials in countries of the Gulf Cooperation Council (GCC)
Bibliographic record
Abstract
Objectives: To describe variations in characteristics of randomized controlled trials conducted in the Gulf Cooperation Council (GCC) countries, and critically appraising the quality of design, conduct and analysis of the trials. Methods: We carried out a systematically comprehensive electronic search of articles published between 1990 and 2018 and indexed in several databases: i) MEDLINE/PubMed, ii) EMBASE, iii) Cochrane Central Register of Controlled Trials (CENTRAL), iv) ClinicalTrials.gov, and v) World Health Organization International Clinical Trials Registry Platform. We summarized the overall risk of bias present in all analyzed studies using the Cochrane Collaboration risk of bias tool (CCRBT). Results: A remarkable shift in numbers of publications from 2006 onwards was found. The largest number of publications were from Saudi Arabia and consisted of hospitals/clinics based studies. Lack of randomization was found in the majority of reports, and nearly three-fourth of the studies involved the use of intention-to-treat (ITT) principle. However, the proportion of adequately generated random sequence methods has increased yearly, and this increase accounted for a relatively large proportion over the latter half of the studied period (p<0.001), in contrast to the proportion of allocation concealment and blinding. Journal impact factor was significantly correlated with the quality of random sequence generation (r=0.145; p=0.014). Conclusion: The randomization methods have gained more attention over the last 3 decades. Secondly, Journal impact factor can serve as an indicator of randomization quality. To mitigate the large rate of overall high risk of bias in GCC studies, high-quality trials must be considered by ensuring adequate allocation concealment and blinding methods. PROSPERO No. ID: CRD42022310331
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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 | 0.186 | 0.438 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.017 |
| Bibliometrics | 0.038 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".