First report of the International Council of Cardiovascular Prevention and Rehabilitation’s Registry (ICRR)
Bibliographic record
Abstract
Objectives Cardiac rehabilitation – programs comprehensively delivering outpatient secondary prevention – is under-available and under-studied in the resource-poor settings where it is needed most. This report summarizes the governance, participating sites, patient characteristics and outcomes, as well as knowledge translation activities during first year of operation of ICCPR’s registry, namely the International Cardiac Rehab Registry.Methods A pilot study was undertaken with five centers, demonstrating feasibility, satisfaction with the on-boarding processes, as well as data quality.Results Fourteen centers have been engaged from all regions but Europe; Data have been entered on >1000 patients (18.1% female; mean age = 57.6), of whom 62.4% completed their programs and 19.9% dropped out for work or clinical reasons. Post-program, completers had significantly better work status, functional capacity, medication adherence, physical activity levels, diet, as well as lower tobacco use than non-completers (all p < 0.05). A site Certification program was developed and piloted, with five centers now recognized for their quality, given they met over 70% of the 13 internationally agreed standards based on Registry data and a virtual site assessment.Conclusion Annual assessments have started. Quality improvement activities will soon be underway. We continue to invite new programs, supporting development in resource-poor settings to the benefit of patients served.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".