Cardiac rehabilitation centers in Romania: Where are we now?
Bibliographic record
Abstract
Purpose: In 2024, Romania is still facing a critical challenge with high cardiovascular disease mortality rates despite extensive research and policy initiatives. Our study sought to examine the actual state of cardiac rehabilitation in Romania by identifying the healthcare facilities that provide and deliver cardiac rehabilitation services. Methods: The research began with a comprehensive investigation into cardiac rehabilitation centers across Romania. This involved leveraging search engines to identify these facilities. Keywords like “cardiac rehabilitation”, “cardiovascular rehabilitation”, and “cardiac rehabilitation centers” were instrumental in pinpointing relevant information, which included names, geographical locations, and contact details of the centers. Upon identification of potential centers, our research team initiated direct engagement with these facilities via telephonic interviews. Results: The data collected in 2024 was compared to previous findings from a 2017 research report to evaluate the progress and impact of prevention efforts over time. While the number of cardiac rehabilitation centers has grown (69.2% increase in the nationwide count of cardiac rehabilitation centers/facilities), an uneven geographic distribution persists, exacerbated by disruptions during the COVID-19 pandemic. Among the cardiac rehabilitation centers identified, 23% are located in balneoclimatic resorts, but the majority of cardiac rehabilitation centers are located in Bucharest. Overall, 65.3% of all identified centers in Romania belong to the private healthcare sector. Among these private centers, 41.1% offer cardiac rehabilitation programs as continuous inpatient care, with durations ranging from 5 to 16 days. All cardiac rehabilitation centers are equipped with the necessary equipment to perform basic cardiological investigations as well as physio-kinesiological rehabilitative procedures, in addition to aerobic physical training (53.8%). In 33.3% of the identified centers, diabetologists and psychologists/psychotherapists are integral members of the multidisciplinary cardiac rehabilitation team, while only 19.2% of the centers include a dietitian/nutritionist. Regarding costs, there is significant variation depending on the geographical area. In Bucharest, a single rehabilitation session costs between 100 to 400 lei (20 to 80 €), and a rehabilitation program spanning 4-6 weeks can cost up to 3350 lei. Comparing costs identified in 2017, we have observed an increase ranging from 47% to 188% in 2024. Conclusions: Despite an increase in the number of cardiac rehabilitation centers, their prevalence remains inadequate to fulfill the demands of the population. Telerehabilitation emerges as a promising solution, with limited adoption in only one center. Cost variations across regions pose a barrier to patient participation. Our article proposes strategies including decision algorithms for personalized recommendations, expanding cardiac rehabilitation centers, and advocating for comprehensive cost reimbursement. The urgent need for collaborative efforts is emphasized, envisioning innovative solutions like mobile applications to foster a sense of community and optimize cardiac rehabilitation, ultimately improving cardiovascular health outcomes in Romania. Keywords: cardiac rehabilitation, telerehabilitation, cardiac rehabilitation centers, healthcare disparities, cardiovascular health
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".