Research trends in cardiac rehabilitation following COVID-19: A cross-sectional bibliometric study
Bibliographic record
Abstract
This bibliometric analysis assesses scientific progress, spatial distribution, keyword trends, thematic evolution, and research gaps in cardiac rehabilitation research (CRR), with a focused appraisal of the post-COVID-19 era (2020-2023). Scopus-indexed publications from 1948 to 2023 and 2020 to 2023 were analyzed using VOSviewer (v1.6.19) and Biblioshiny (v2.0.2). The study was strengthening the reporting of observational studies in epidemiology-compliant. A total of 9173 CRR documents were identified, showing sustained exponential growth over time. The Journal of Cardiopulmonary Rehabilitation and Prevention emerged as the leading source. The United State, Canada, and the United Kingdom led global output and collaboration networks. Core keywords included "cardiac rehabilitation," "coronary artery disease," "myocardial infarction," "exercise," and "rehabilitation." Post-COVID-19 analyses revealed a discernible thematic shift, with emerging clusters and hot themes centered on "age," "primary care," "heart transplant," and "exercise". This first comprehensive bibliometric overview of CRR maps long-term growth, geographic leaders, evolving themes, and research gaps, and highlights a reorientation of priorities in the post-COVID-19 era to inform future research directions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.067 | 0.099 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".