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Record W2401136257 · doi:10.1097/hco.0000000000000263

Heart failure and sleep-disordered breathing

2016· review· en· W2401136257 on OpenAlexaff
Alexander Zhai, Amelia Yip, Majed Haddad

Bibliographic record

VenueCurrent Opinion in Cardiology · 2016
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineHeart failureContinuous positive airway pressureBreathingPositive airway pressureContext (archaeology)Sleep disordered breathingCardiologyIntensive care medicineInternal medicinePopulationSleep (system call)Central sleep apneaCheyne–Stokes respirationObstructive sleep apneaPolysomnographyAnesthesiaApnea

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Sleep-disordered breathing, which includes both obstructive and central sleep apnoea (OSA and CSA, respectively), is highly prevalent in patients with heart failure. In this review, we outline our current understanding of the bidirectional relationship between these disorders and heart failure. We also explore the role of recent advances in therapeutics. RECENT FINDINGS: Although early studies suggest promise of adaptive servoventilation in treating sleep-disordered breathing, particularly CSA with associated Cheyne-Stokes respiration, the recent clinical trial in the heart failure patient population has demonstrated worse cardiovascular outcome in symptomatic patients. SUMMARY: Both OSA and CSA are highly prevalent in patients with heart failure. Effective treatment of OSA with continuous positive airway pressure can improve cardiovascular outcome in these patients. However, recent evidence suggests that adaptive servoventilation cannot be safely recommended as a therapy for CSA in the context of heart failure, as a result of increased risk of cardiovascular mortality.

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)0.002
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.092
GPT teacher head0.425
Teacher spread0.333 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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