Implementation of a comprehensive intervention for patients at high risk of cardiovascular disease in rural China: A pragmatic cluster randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: This study aims to assess whether a standard intervention package of cardiovascular disease (CVD) care was being delivered effectively, and if it was associated with improved lifestyle and biomedical indicators. METHODS: In rural China, we implemented a pragmatic cluster randomized controlled trial for 12 months, randomized at the township hospital level, and compared with usual care. Intervention case management guideline, training and performance monitoring meeting and patient support activities were designed to fit within the job description of family doctors in the township hospitals and comprised: 1) prescription of a standardised package of medicines targeted at those with hypertension or diabetes; 2) advice about specific lifestyle interventions; and 3) advice about medication adherence. Participants were 50-74 years old, had hypertension and CVD risk scores >20% or diabetes, but were excluded if a history of severe CVD events. We also randomly selected 100 participants from six selected clusters per arm as a panel to collect intermediate biomedical indicators over time. RESULTS: A total of 28,130 participants, in 33 intervention and 34 control township hospitals, were recruited. Compared with the control arm, participants in the intervention arm had substantially improved prescribing rates of anti-hypertensives, statins and aspirin (P<0.001), and had higher medication taking rates of aspirin and statins (P<0.001). Mean systolic and diastolic blood pressures were similar across both arms (0.15 mmHg, P = 0.79, and 0.52 mmHg, P = 0.05, respectively). In the panel, (950) rates of smoking (OR = 0.23, P = 0.02) and salt intake (OR = 2.85, P = 0.03) were significantly reduced in the intervention versus control arms, but there were no statistically significant improvement over the 12 month follow-up period in biomedical indicators (P>0.05). CONCLUSION: Implementation of the package by family doctors was feasible and improved prescribing and some lifestyle changes. Additional measures such as reducing medication costs and patient education are required. TRIAL REGISTRATION: Current Controlled Trials ISRCTN58988083.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".