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Record W4200416347 · doi:10.1080/14779072.2021.2013812

Cardiac rehabilitation following coronary artery dissection: recommendations and patient considerations

2021· review· en· W4200416347 on OpenAlexaff
Rohit Samuel, Mesfer Alfadhel, Cameron McAlister, Thomas Nestelberger, Jacqueline Saw

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2021
Typereview
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsUniversity of British ColumbiaVancouver General Hospital
FundersHigher Education Funding Council for England
KeywordsScadMedicinePsychosocialCoronary artery diseaseMaceArtery dissectionPopulationAcute coronary syndromeIntensive care medicineQuality of life (healthcare)RehabilitationCardiologyInternal medicinePhysical therapyPercutaneous coronary interventionMyocardial infarctionCoronary angiographyNursing

Abstract

fetched live from OpenAlex

INTRODUCTION: : Cardiac rehabilitation (CR) is a multidisciplinary intervention for secondary prevention, improving functional capacity, enhancing quality of life, and improving psychosocial wellbeing in broad range of cardiovascular disease. It has been well studied over a number of years and is a Class I recommendation in multiple guidelines. However, there is a paucity of data regarding the usefulness of CR in patients with spontaneous coronary artery dissection (SCAD). AREAS COVERED: : This narrative review aims to give an overview of the evidence underpinning CR as well as the pathophysiological mechanisms of SCAD and how they relate to exercise and shear stress. Furthermore, the evidence of the usefulness of CR in the SCAD population will be reviewed. EXPERT OPINION: : Traditional CR programs are safe and effective in SCAD cohorts, however SCAD specific CR (SCAD-CR) has significant benefits including reductions in MACE. The principles of SCAD-CR should be applied to any CR for SCAD patients for optimal outcomes and minimization of harm.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.015
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.352
Teacher spread0.320 · 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.

Study designOther design
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

Citations12
Published2021
Admission routes1
Has abstractyes

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