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Record W4297237248 · doi:10.15420/cfr.2022.16

Why Do so Few People with Heart Failure Receive Cardiac Rehabilitation?

2022· review· en· W4297237248 on OpenAlexaff
David R. Thompson, Chantal F. Ski, Alexander M. Clark, Hasnain Dalal, R. Taylor

Bibliographic record

VenueCardiac failure review · 2022
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsAthabasca University
FundersNational Institute for Health and Care Research
KeywordsPsychosocialRehabilitationHeart failureMedicineGuidelinePhysical therapyPhysical medicine and rehabilitationPsychiatryCardiology

Abstract

fetched live from OpenAlex

Many people with heart failure do not receive cardiac rehabilitation despite a strong evidence base attesting to its effectiveness, and national and international guideline recommendations. A more holistic approach to heart failure rehabilitation is proposed as an alternative to the predominant focus on exercise, emphasising the important role of education and psychosocial support, and acknowledging that this depends on patient need, choice and preference. An individualised, needs-led approach, exploiting the latest digital technologies when appropriate, may help fill existing gaps, improve access, uptake and completion, and ensure optimal health and wellbeing for people with heart failure and their families. Exercise, education, lifestyle change and psychosocial support should, as core elements, unless contraindicated due to medical reasons, be offered routinely to people with heart failure, but tailored to individual circumstances, such as with regard to age and frailty, and possibly for recipients of cardiac implantable electronic devices or left ventricular assist devices.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.304
Teacher spread0.286 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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