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Record W4411669591 · doi:10.1038/s44325-025-00062-w

A century of cardiac rehabilitation research: Bibliometric review of publication history, keyword trends, and citations

2025· article· en· W4411669591 on OpenAlexaff
Deborah Manandi, Karice Hyun, Dion Candelaria, Matthew Hollings, Qiang Tu, Sarah Gauci, Adrienne O’Neil, Georgia K. Chaseling, Ling Zhang, Tom Briffa, Sherry L. Grace, Robyn Gallagher, Julie Redfern

Bibliographic record

Venuenpj Cardiovascular Health · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsToronto Rehabilitation InstituteYork UniversityUniversity Health Network
FundersNational Health and Medical Research CouncilNational Heart Foundation of AustraliaAustralian Government
KeywordsKeyword searchBibliometricsRehabilitationLibrary scienceMedicineInformation retrievalComputer sciencePhysical therapy

Abstract

fetched live from OpenAlex

Research into cardiac rehabilitation (CR), a key model for secondary prevention of cardiovascular disease, has evolved since first described in 1927. This review aimed to explore this evolution by identifying CR-related publications from the Web of Science Core Collection and summarizing CR research publication history, trends in publication keywords, and citations over time. A total of 8729 CR publications appeared across 1441 journals (median impact factor: 2.6) and were cited 315,819 times, with over 85% (7455/8729) published in the past two decades. These publications involved contributions from 26,909 authors across 120 countries, despite disproportionate domination by high-income countries. Publication keywords have consistently focused on exercise but have evolved from evaluating clinical events, quality-of-life, and return-to-work outcomes to improving accessibility using digital interventions. However, a broader focus on other cardiovascular risk factors, comorbidities, and various research designs may be needed to modernize CR, particularly in lower-income countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.805
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0190.031
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.407
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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