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Record W3116795577 · doi:10.1161/strokeaha.120.031709

Trials in Sleep Apnea and Stroke

2020· review· en· W3116795577 on OpenAlexaff
Mark I. Boulos, Laavanya Dharmakulaseelan, Devin L. Brown, Richard H. Swartz

Bibliographic record

VenueStroke · 2020
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineContinuous positive airway pressureStroke (engine)Randomized controlled trialObstructive sleep apneaSleep apneaPopulationClinical trialPhysical therapyApneaIntensive care medicineEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

Few randomized controlled trials have evaluated the effectiveness of continuous positive airway pressure (CPAP) in reducing recurrent vascular events and mortality in poststroke obstructive sleep apnea (OSA). To date, results have been mixed, most studies were underpowered and definitive conclusions are not available. Using lessons learned from prior negative trials in stroke, we reappraise prior randomized controlled trials that examined the use of CPAP in treating poststroke OSA and propose the following considerations: (1) Intervention-based changes, such as ensuring that patients are using CPAP for at least 4 hours per night (eg, through use of improvements in CPAP technology that make it easier for patients to use), as well as considering alternative treatment strategies for poststroke OSA; (2) Population-based changes (ie, including stroke patients with severe and symptomatic OSA and CPAP noncompliers); and (3) Changes to timing of intervention and follow-up (ie, early initiation of CPAP therapy within the first 48 hours of stroke and long-term follow-up calculated in accordance with sample size to ensure adequate power). Given the burden of vascular morbidity and mortality in stroke patients with OSA, there is a strong need to learn from past negative trials and explore innovative stroke prevention strategies to improve stroke-free survival.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.000
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 head0.103
GPT teacher head0.407
Teacher spread0.304 · 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.

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

Citations34
Published2020
Admission routes1
Has abstractyes

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