MétaCan
Menu
Back to cohort
Record W4310796488 · doi:10.1016/j.cjco.2022.11.022

Comparing the Effectiveness of 2 Cardiac Rehabilitation Exercise Therapy Programs

2022· article· en· W4310796488 on OpenAlexaffabout
Tiffany Yuen, David M. Buijs, Yongzhe Hong, Andrea Van Damme, T Meyer, Jeevan Nagendran, Gábor Gyenes

Bibliographic record

VenueCJC Open · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsInstitute of Health EconomicsUniversity of Alberta
Fundersnot available
KeywordsRehabilitationMedicinePhysical therapyCardiovascular healthDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Cardiovascular diseases are among the leading causes of morbidity and mortality in Canada, highlighting the critical role of disease prevention and risk reduction programs. Cardiac rehabilitation (CR) is a key component of comprehensive cardiovascular care. Currently, more than 200 CR programs are established across the country, varying in duration, number of in-person supervised exercise sessions, and recommendations for exercise frequency at-home. In an increasingly cost-conscious healthcare environment, the effectiveness of healthcare services must be consistently reevaluated. This study evaluates the impact of 2 CR programs implemented by the Northern Alberta Cardiac Rehabilitation Program, by comparing peak metabolic equivalents achieved by study participants in each program. We hypothesize that our "hybrid" CR program, which is structured as an 8-week program with weekly in-person exercise sessions and a prescribed home exercise program, has patient outcomes similar to those of our "traditional" CR program, which required biweekly in-person exercise sessions over the course of 5 weeks. The results of this study may have implications for evaluating how to minimize barriers to both rehabilitation participation and long-term effectiveness of CR programs. The results may help inform the structuring and funding of future rehabilitation programs.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.375
Teacher spread0.339 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCJC OpenSame topicCardiac Health and Mental HealthFrench-language works237,207