Development of the Double-Blind, Randomized Trials of Effects of Antihypertensive Medicines (DREAM) Database and Characteristics of the Included Trials: Protocol for an Umbrella Review and Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: A comprehensive evaluation of short-term effects of antihypertensive medicines is important for informing guidelines and clinical practice. OBJECTIVE: We aimed to develop the Double-blind Randomized trials of Effects of Antihypertensive Medicines (DREAM) database to facilitate a series of meta-analyses evaluating the short-term effects of antihypertensive medicines. METHODS: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, and Epistemonikos from inception until December 2022 to identify relevant randomized clinical trials (RCTs). We included RCTs in the DREAM database if they were double-blind, enrolled adult participants, evaluated the 5 major classes of antihypertensive medicines over a duration of 2 to 26 weeks, and were published in the English language. Screening of records for inclusion and data collection were both conducted in duplicate. The planned meta-analyses using the DREAM database will follow standard methods as recommended by the Cochrane Handbook for Systematic Reviews. The general methods for these meta-analyses are outlined. RESULTS: The DREAM database includes 1623 RCTs (4359 comparisons), of which 44% (707/1623) were placebo-controlled, 70% (1141/1623) had parallel-group allocation, and 37% (607/1623) had 3 or more randomized groups. A total of 304,253 participants (mean age 54 years; 509/1623, 46% female) were included, 86% (1391/1623) of RCTs had participants with hypertension, and 11% (175/1623) of RCTs had participants with cardiovascular disease at baseline. RCTs with at least 1 group randomized to combination therapy accounted for 23%(371/1623). The median duration of treatment was 8 weeks. Most (93%, 1509/1623) RCTs reported data on effects on blood pressure. CONCLUSIONS: The first series of meta-analyses using the DREAM database will assess the effects of antihypertensive medicines on blood pressure and safety outcomes, including effects on headache, and cardiovascular events. The findings of these meta-analyses will inform clinical practice guidelines and help identify priorities for future research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65205.
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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 | 0.148 | 0.245 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.023 | 0.023 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.061 | 0.012 |
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".