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Evaluation and Treatment of Central Sleep Apnea in Patients with Heart Failure

2022· review· en· W4296311546 on OpenAlexaff
Marat Fudim, Izza Shahid, Sitaramesh Emani, Liviu Klein, Kara Dupuy-McCauley, Shelley Zieroth, Robert J. Mentz

Bibliographic record

VenueCurrent Problems in Cardiology · 2022
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsSt. Boniface HospitalUniversity of Manitoba
FundersNational Heart, Lung, and Blood InstituteAmerican Heart Association
KeywordsMedicineHeart failureSleep apneaCentral sleep apneaComorbidityIntensive care medicineContinuous positive airway pressurePositive airway pressureObstructive sleep apneaModalitiesPopulationCohortInternal medicineCardiologyPolysomnographyApnea

Abstract

fetched live from OpenAlex

Sleep-disordered breathing (SDB) is a common comorbidity in patients with heart failure (HF). Prevalence of the most common subtypes of SDB, central sleep apnea (CSA) and obstructive sleep apnea (OSA), is increasing, which is concerning due to the association of SDB with increased mortality in patients with HF. Despite an increasing burden of CSA in HF, it is difficult to detect using current diagnostic tools and the treatment modalities are limited by variable efficacy and patient adherence. Though positive airway pressure therapies remain the cornerstone of OSA treatment, the management of CSA in the setting of HF continues to evolve. The association of the presence of CSA with worse prognosis in HF patients warrants the need for routine screening for signs and symptoms of CSA in this population. In this review, we examine the connection between CSA and HF, and highlight advancements in timely diagnostics, treatment modalities, and strategies to promote facilitation of compliance in this high-risk cohort.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.077
GPT teacher head0.370
Teacher spread0.293 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

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