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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.005
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.001

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