Secondary prevention programmes for coronary heart disease: a meta-regression showing the merits of shorter, generalist, primary care-based interventions
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine which programme characteristics influence the effectiveness of secondary prevention programmes for Coronary Heart Disease. DESIGN: The study follows a meta-regression design. METHODS: We conducted a meta-regression within a systematic review of randomized trials comparing secondary prevention programmes versus usual care. The primary outcome was all-cause mortality. Studies were identified by searching multiple electronic databases, bibliographies of published studies, contact with experts, and references provided by the United States Centers for Medicare and Medicaid Services. Primary authors of all relevant trials were surveyed for detailed information on programme characteristics. Forty-six unique trials were identified (18 821 patients). The pooled all-cause mortality risk ratio (RR) for programmes was 0.87 [95% confidence interval (CI) 0.79-0.97]. Programmes containing less than 10 h of patient contact with health professionals reduced all-cause mortality (RR 0.80, 95% CI 0.68-0.95) as effectively as programmes with more contact time. Programmes provided in general practice settings were effective at reducing all-cause mortality (RR 0.76, 95% CI 0.63-0.92) and compared favourably with the effectiveness of hospital-based programmes. Other characteristics, including specialist versus generalist provision, did not appreciably impact programme effectiveness. CONCLUSIONS: Shorter secondary prevention programmes, those based in general practice, and those staffed by generalists are at least as effective in reducing all cause mortality in patients with coronary heart disease as longer programmes, hospital-based programmes, and programmes staffed by specialists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.052 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".