Rationale and design of the improving Care for Cardiovascular Disease in China (CCC) project: a national registry to improve management of atrial fibrillation
Bibliographic record
Abstract
INTRODUCTION: Inadequate management of patients with atrial fibrillation (AF) has been reported in China for anticoagulation therapy and treatment for concomitant diseases. An effective quality improvement programme has been lacking to promote the use of evidence-based treatments and improve outcome in patients with AF. METHODS AND ANALYSIS: The Improving Care for Cardiovascular Disease in China-AF programme is a collaboration of the American Heart Association and the Chinese Society of Cardiology. This programme is designed to promote adherence to AF guideline recommendations and outcomes for inpatients with AF. Launched in February 2015, 150 hospitals are recruited by geographic-economic regions across 30 provinces in China. Each month, 10-20 inpatients with AF are enrolled in each hospital. A web-based data collection platform is used to collect clinical information for patients with AF, including patients' demographics, admission information, medical history, in-hospital care and outcomes, and discharge medications for managing AF. The quality improvement initiative includes monthly benchmarked reports on hospital quality, training sessions, regular webinars and recognitions of hospital quality achievement. Primary analyses will include adherence to performance measures and guidelines. To address intrahospital correlation, generalised estimating equation models will be applied. As of March 2017, 28 801 AF inpatients have been enrolled. ETHICS AND DISSEMINATION: This study protocol was approved by the Ethics Committee of Beijing Anzhen Hospital, Capital Medical University. Results will be published in peer-reviewed medical journals. TRIAL REGISTRATION NUMBER: NCT02309398.
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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 | 0.125 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".