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Record W7116906167 · doi:10.1016/j.cjca.2025.10.003

Cardiac Rehabilitation After Thoracic Aortic Surgery

2025· article· en· W7116906167 on OpenAlexafffundvenue
M. Sean McMurtry, Rachel J. Skow, Stephen Foulkes, Nathaniel Moulson, James McKinney, Richard B. Thompson, Mark Haykowsky

Bibliographic record

VenueCanadian Journal of Cardiology · 2025
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
FundersUniversity Hospital FoundationUniversity of AlbertaFaculty of Medicine and Dentistry, University of AlbertaHeart and Stroke Foundation of Canada
KeywordsThoracic aortic aneurysmRehabilitationCardiothoracic surgeryAortic surgeryThoracic aortaMagnetic resonance imagingAortic aneurysmCardiac surgeryCardiac magnetic resonance

Abstract

fetched live from OpenAlex

Thoracic aortic diseases, including aortic aneurysm and aortic dissection, are disorders that frequently require cardiac surgical intervention. Current clinical practice guidelines recommend cardiac rehabilitation for patients after thoracic aortic surgery. However, the evidence to support these recommendations remains limited, and there is a notable absence of individualized approaches for resuming regular exercise. In this narrative review the literature on cardiovascular rehabilitation after thoracic aortic surgery for inherited and acquired thoracic aortic disease is examined, a clinical case to illustrate limitations in the current approach is presented, and how exercise-based magnetic resonance imaging to assess aortic wall stress might support a more personalized and precise exercise prescription is explored. Further research on the safety and efficacy of exercise training in patients after thoracic aortic surgery-particularly randomized controlled trials-are needed.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Cardiology→Same topicAortic Disease and Treatment Approaches→French-language works237,207→