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Determinants of exercise intolerance in patients with heart failure and reduced or preserved ejection fraction

2015· review· en· W2331955915 on OpenAlexaff
Mark J. Haykowsky, Corey R. Tomczak, Jessica M. Scott, D. Ian Paterson, Dalane W. Kitzman

Bibliographic record

VenueJournal of Applied Physiology · 2015
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
FundersNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsExercise intoleranceHeart failureEjection fractionCardiologyInternal medicineHeart failure with preserved ejection fractionMedicinePhysical exerciseStroke volume

Abstract

fetched live from OpenAlex

This mini-review summarizes the literature regarding the mechanisms of exercise intolerance in patients with heart failure and reduced or preserved ejection fraction (HFREF and HFPEF, respectively). Evidence to date suggests that the reduced peak pulmonary oxygen uptake (pulm V̇o₂) in patients with HFREF compared with healthy controls is due to both central (reduced convective O₂ transport) and peripheral factors (impaired skeletal muscle blood flow, decreased diffusive O₂ transport coupled with abnormal skeletal morphology, and metabolism). Although central and peripheral impairments also limit peak pulm V̇o₂ in HFPEF patients compared with healthy controls, emerging data suggest that the latter may play a relatively greater role in limiting exercise performance in these patients. Unlike HFREF, currently there is limited evidence-based therapies that improve exercise capacity in HFPEF patients, therefore future studies are required to determine whether interventions targeted to improve peripheral vascular and skeletal muscle function result in favorable improvements in peak pulm and leg V̇o2 and their determinants in HFPEF patients.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · 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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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 designSystematic review
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

Citations189
Published2015
Admission routes1
Has abstractyes

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