SLEAP SMART (Sleep Apnea Screening Using Mobile Ambulatory Recorders After TIA/Stroke): A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Poststroke/transient ischemic attack obstructive sleep apnea (OSA) is prevalent, linked with numerous unfavorable health consequences, but remains underdiagnosed. Reasons include patient inconvenience and costs associated with use of in-laboratory polysomnography (iPSG), the current standard tool. Fortunately, home sleep apnea testing (HSAT) can accurately diagnose OSA and is potentially more convenient and cost-effective compared with iPSG. Our objective was to assess whether screening for OSA in patients with stroke/transient ischemic attack using HSAT, compared with standard of care using iPSG, increased diagnosis and treatment of OSA, improved clinical outcomes and patient experiences with sleep testing, and was a cost-effective approach. METHODS: We consecutively recruited 250 patients who had sustained a stroke/transient ischemic attack within the past 6 months. Patients were randomized (1:1) to use of (1) HSAT versus (2) iPSG. Patients completed assessments and questionnaires at baseline and 6-month follow-up appointments. Patients diagnosed with OSA were offered continuous positive airway pressure. The primary outcome was compared between study arms via an intention-to-treat analysis. RESULTS: =0.04) compared with the iPSG arm. Furthermore, patients assigned to HSAT, compared with iPSG, were more likely to be prescribed continuous positive airway pressure (40.0% versus 27.2%), report significantly reduced sleepiness, and a greater ability to perform daily activities. Moreover, a significantly greater proportion of patients reported a positive experience with sleep testing in the HSAT arm compared with the iPSG arm (89.4% versus 31.1%). Finally, a cost-effectiveness analysis revealed that HSAT was economically attractive for the detection of OSA compared with iPSG. CONCLUSIONS: In patients with stroke/transient ischemic attack, use of HSAT compared with iPSG increases the rate of OSA diagnosis and treatment, reduces daytime sleepiness, improves functional outcomes and experiences with sleep testing, and could be an economically attractive approach. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02454023.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".