Outcome measures for randomised clinical trials and multicentre observational studies of cardiovascular diseases published in major clinical journals: systematic review and evidence mapping
Bibliographic record
Abstract
BACKGROUND: Outcome measure choice and definition can determine the result of the study. We describe outcome measures and their definitions for cardiovascular studies in highly cited medical journals. METHODS: between 1 January 2013 and 6 June 2024 from Embase and Ovid Medline were included. Two independent reviewers selected the studies and extracted the primary and secondary outcome measures from each publication. RESULTS: 386 studies (83% RCTs; 17% observational) representing 10 699 147 participants were included. Studies investigated coronary heart disease (51%), cardiomyopathy/heart failure (22%), heart rhythm disease (15%), valvular heart disease (11%) and 'other' cardiovascular diseases (1%), with 45% investigating a device and 48% funded by industry. The most frequently reported primary outcome measure was a composite (63%), the most frequent component of which was myocardial infarction (58%). The use of a composite for the primary outcome measure increased from 49% of studies in 2013 to a peak of 85% in 2018. From 2013 to 2023, the median number of secondary outcome measures per study increased for RCTs (3-8) and observational studies (0-7). Definitions for cardiovascular mortality, myocardial infarction and stroke varied across the studies. CONCLUSIONS: For cardiovascular studies published in highly cited journals, there has been an expansion in the use of primary composite outcome measures and secondary outcome measures, with heterogeneity in the definition of primary outcome measures. A standardised approach to the use of cardiovascular outcomes measures is required.
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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 | 0.181 | 0.460 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.019 |
| Bibliometrics | 0.050 | 0.042 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".