Cardiac rehabilitation availability and delivery in Europe: How does it differ by region and compare with other high-income countries?
Bibliographic record
Abstract
The aims of this study were to establish cardiac rehabilitation availability and density, as well as the nature of programmes, and to compare these by European region (geoscheme) and with other high-income countries. A survey was administered to cardiac rehabilitation programmes globally. Cardiac associations were engaged to facilitate programme identification. Density was computed using global burden of disease study ischaemic heart disease incidence estimates. Four high-income countries were selected for comparison (N = 790 programmes) to European data, and multilevel analyses were performed. Cardiac rehabilitation was available in 40/44 (90.9%) European countries. Data were collected in 37 (94.8% country response rate). A total of 455/1538 (29.6% response rate) programme respondents initiated the survey. Programme volumes (median 300) were greatest in western European countries, but overall were higher than in other high-income countries (P < 0.001). Across all Europe, there was on average only 1 CR spot per 7 IHD patients, with an unmet regional need of 3,449,460 spots annually. Most programmes were funded by social security (n = 25, 59.5%; with significant regional variation, P < 0.001), but in 72 (16.0%) patients paid some or all of the programme costs (or ∼18.5% of the ∼€150.0/programme) out of pocket. Guideline-indicated conditions were accepted in 70% or more of programmes (lower for stable coronary disease), with no regional variation. Programmes had a multidisciplinary team of 6.5 ± 3.0 staff (number and type varied regionally; and European programmes had more staff than other high-income countries), offering 8.5 ± 1.5/10 core components (consistent with other high-income countries) over 24.8 ± 26.0 hours (regional differences, P < 0.05). European cardiac rehabilitation capacity must be augmented. Where available, services were consistent with guidelines, but varied regionally.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".