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Record W2922272023 · doi:10.5206/uwomj.v85i2.4146

A review of issues concerning implementation of cardiac rehabilitation programs

2016· review· en· W2922272023 on OpenAlexvenueno aff
Stuart Danby, Charles Yin

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2016
Typereview
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsReferralRehabilitationPsychological interventionMedicineIntervention (counseling)Secondary preventionWork (physics)Myocardial infarctionPhysical therapyIntensive care medicineMedical emergencyNursingCardiologyEngineeringInternal medicine

Abstract

fetched live from OpenAlex

Cardiac rehabilitation (CR) is a tertiary preventative intervention for patients who have experienced adverse cardiac events such as a myocardial infarction (MI). Interdisciplinary CR teams focus on reducing multiple risk factors by addressing diet, exercise, and behavior change. CR programs have proven to be comparable in cost to other interventions for cardiac diseases, however the appropriate delivery of these programs faces multiple issues. One major barrier to effective delivery of CR programs is the lack of referral by the health care teams who assess cardiac patients. Other barriers are funding, team structure, and patient access to these programs. This article examines the issues facing CR delivery and possible ways to address them. Given the evidence for its efficacy and cost effectiveness, physicians should work to ensure proper CR referral and improved program delivery.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.045
GPT teacher head0.403
Teacher spread0.357 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2016
Admission routes1
Has abstractyes

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