Obstructive sleep apnea is associated with cognitive impairment in minor ischemic stroke
Bibliographic record
Abstract
OBJECTIVES: Obstructive sleep apnea (OSA) is commonly seen in stroke patients, and its relationship with cognitive impairment remains poorly understood. This study aimed to explore the roles of OSA in cognition impairment in patients with minor ischemic stroke. METHODS: Patients with minor ischemic stroke were consecutively enrolled from January 2020 to May 2021. Every patient underwent polysomnography (PSG) to assess for OSA. Based on the apnea hypopnea index (AHI), patients were grouped into the no OSA (AHI < 5), mild OSA (5 ≤ AHI < 15), and moderate-to-severe OSA (MS OSA, AHI ≥ 15) groups. Neuropsychological assessments were performed to evaluate cognitive function, and the correlations between cognitive function and OSA were investigated. RESULTS: Of 94 patients, 35 had no OSA, 32 had mild OSA, and 27 had moderate-to-severe OSA. Compared to the no or mild OSA groups, the moderate-to-severe OSA group performed worse on the Chinese version of the Auditory Verbal Learning Test (CAVLT)-Recognition (p < 0.001), Digital Span Test (DST)-Backward (p < 0.001), Montreal Cognitive Assessment (MoCA) (p < 0.001), and Stroop Color and Word Test (SCWT)-Interference (p < 0.001). The severity of cognitive impairment was assessed using the MoCA, which was negatively related to the AHI (p = 0.041) and lowest SpO2 (p = 0.048). CONCLUSIONS: The findings suggest that OSA has significant effects on cognition impairment in patients with minor ischemic stroke and that hypoxemia may be a potential pathophysiological mechanism of OSA-induced cognitive impairment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".