Obstructive Sleep Apnea as an Independent Stroke Risk Factor
Bibliographic record
Abstract
BACKGROUND: Stroke is a leading cause of death and disability affecting nearly 800,000 people in the United States every year. Obstructive sleep apnea (OSA) is found in over 60% of patients with stroke/transient ischemic attack (TIA) and identified as an independent stroke risk factor in large epidemiology studies and Canadian Stroke Prevention Guidelines (SPG) but not in the United States. The 2014 Secondary SPG recommend OSA screening and treatment as a consideration only, not a requirement. The twofold purpose of this article is, first, to present the evidence supporting OSA as an independent stroke risk factor in national SPG with mandatory recommendations and, second, to engage neuroscience nurses to incorporate OSA assessment and interventions into the nursing process and thereby promote excellence in stroke/TIA patient care. METHODS: A systematic literature search was conducted in Medline, CINAHL, and PubMed to identify research from 2003 through 2013 on the independent risk, mortality, and prevalence relationship between OSA and stroke/TIA including recurrence and recovery outcomes with continuous positive airway pressure (CPAP) therapy. RESULTS: Twenty-eight research articles were reviewed: 14 observational cohorts, five case-control studies, four cross-sectional studies, and four randomized control trials representing 12 countries and 10,671 subjects. DISCUSSION: OSA is highly prevalent in patients with stroke/TIA independently increasing stroke risk. CPAP studies revealed reduced stroke recurrence and improved recovery with feasible initiation in stroke units. Patients with stroke/TIA have less OSA-associated daytime sleepiness and obesity, making the usual screening tools insufficient and CPAP adherence challenging. Treating OSA decreases stroke prevalence and mortality. OSA initiatives empower neuroscience nurses to integrate this OSA evidence into clinical practice and improve stroke/TIA patient outcomes.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".