Recruitment of immigrant and ethnic minorities in primary prevention trials of cardiovascular disease
Bibliographic record
Abstract
BACKGROUND: The risk of cardiovascular disease (CVD) may differ across ethnic groups, including those who immigrate to Canada, USA and the UK. Accordingly, the absolute and relative benefits of CVD prevention strategies evaluated in randomized clinical trials (RCTs) may differ by the ethnic and immigrant composition of study participants. METHODS: We searched MEDLINE, EMBASE and Cochrane databases for RCTs of primary prevention strategies for CVD, published between 1980 and December 2009. We only included RCTs of a CVD primary prevention strategy comprising at least 100 participants aged >19 years, and those published in English. We abstracted data on study and participant characteristics, interventions and outcomes, as well as a description of the immigrants and ethnic composition of the participants. We also recorded whether a study was stratified by immigrant and/or ethnic subgroups in evaluating the efficacy of the study intervention. RESULTS: Out of 45 RCTs that met the selection criteria, 11 (24.4%, 95% CI: 14.3-38.8) included and/or reported on the ethnic status of the participants. There were 140,764 persons enrolled in these 11 RCTs, with CVD and/or CVD-related death as the primary outcome, evaluated over a median duration of 5 years. In all 11 trials, the weighted proportion of participants of non-White ethnicity was 10.3% (95% CI: 6.8-15.4). Asian or Asian Pacific ancestry comprised 2.0% (95% CI: 1.1-3.9) of all participants in the five trials that reported details about this group. In no study was the therapeutic efficacy of the intervention was stratified by ethnicity, and none reported on the number of participants who were immigrants. CONCLUSION: RCTs of CVD prevention strategies either rarely recruit or rarely report on the ethnic and immigrant status of their participants. Evaluation of the relative efficacy of CVD prevention strategies should also consider these criteria.
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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 | 0.099 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".