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Record W2577788732 · doi:10.1097/hco.0000000000000370

The impact of obesity on heart failure

2017· review· en· W2577788732 on OpenAlexaff
Alexander Zhai, Haissam Haddad

Bibliographic record

VenueCurrent Opinion in Cardiology · 2017
Typereview
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineObesityHeart failureObesity paradoxWeight lossMechanism (biology)Intensive care medicinePopulationCardiologyInternal medicineEnvironmental healthOverweight

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Obesity, a growing global health problem, contributes to the development of heart failure. However, increased BMI seems protective for those with established disease, a phenomenon known as the 'obesity paradox'. In this review, we outline the mechanism through which obesity can contribute to the development of heart failure, explore the concept of obesity paradox, and highlight the challenges that obesity presents for advanced heart failure therapy. RECENT FINDINGS: Although the mechanism underlying the obesity paradox is complex, meta-analysis shows that intentional weight loss through bariatric surgery can indeed improve cardiac structure and function. With regard to ventricular assist device therapy in obese patients, recent studies demonstrate that while obesity was indeed associated with higher likelihood of complications, there were no statistically significant differences in terms of mortality or delisting from cardiac transplant waiting list. SUMMARY: Obesity is strongly associated with the development of heart failure, through direct and indirect mechanisms. Although clear consensus regarding weight reduction in this patient population is lacking, there is mounting clinical evidence that intentional weight loss may be beneficial, in spite of the well-recognized obesity paradox, particularly as the presence of obesity presents unique challenges in the advanced therapy of heart failure 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 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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.911
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.166
GPT teacher head0.461
Teacher spread0.295 · 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.

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

Citations42
Published2017
Admission routes1
Has abstractyes

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