An inpatient program for diagnosing and treating sleep apnea in patients with stroke
Bibliographic record
Abstract
Background and Purpose: Sleep apnea (SA) is highly prevalent in the stroke population. Testing and treating SA in stroke patients is challenging due to inaccessibility to testing and impaired mobility. To address this problem, we designed and implemented an inpatient diagnosis and treatment program for managing SA in patients with stroke. Our main aim in this article is to assess the feasibility of this program. Methods: We tested 83 patients with a portable SA testing device and initiated treatment with auto-titrating continuous positive airway pressure (A-CPAP) for those who were diagnosed with SA during their stay in an inpatient stroke rehabilitation unit (SRU). Patients diagnosed with SA were given a 2- to 4-week trial of A-CPAP in their hospital bed with close follow-up from a sleep medicine service. Results: Of the 83 patients tested, 54 (67.5%) had SA and 46 (85%) agreed to a trial of A-CPAP therapy. Of the 46 patients, who trialed A-CPAP, 32 (70%) achieved average daily use of four or more hours and went home with it. Conclusions: This program provides a feasible and convenient means of testing and treating SA among stroke patients undergoing inpatient stroke rehabilitation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".