Rehabilitación cardíaca en Latinoamérica: una revisión temática y análisis bibliométrico
Bibliographic record
Abstract
Introduction: Cardiac rehabilitation has been considered a treatment with a high level of evidence and degree of recommendation to reduce cardiovascular morbidity and mortality. In Latin America, the corpus of scientific knowledge about this treatment is not clear, so the objective of this work is to carry out bibliometric analysis on scientific publications related to cardiac rehabilitation in the Latin American context. Materials and Methods: A delimited search strategy was applied in Scopus for documents indexed until September 2023. The records found were analyzed through the VantagePoint software (Search Technology, 15.2) to obtain indicators of activity (frequency of publication, countries, institutions and authors), relationship (co-authorship between countries and authors), and research trends using keywords included by the authors in each article. Results: 124 records were selected. The highest frequency of publication was between 2019 and 2023. The countries with the highest number of publications were Brazil, Canada and Colombia. In terms of co-authorship relationship, these same countries and the United States published the largest number of studies with international collaboration. Finally, most of the registries related cardiovascular rehabilitation with cardiovascular disease (coronary heart disease), exercise, and questionnaires. Discussion and Conclusions: This paper presents results that help guide future studies taking into account publication trends and potential collaborators in the region.
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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 | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.041 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.244 | 0.233 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".