Relationship between sleep disorders and the prognosis of neurological function after stroke
Bibliographic record
Abstract
Objective This study aims to investigate the effects of sleep disorders on the prognosis of neurological function after stroke and other factors affecting the prognosis after stroke. Method We designed a cohort study. A total of 1,542 patients with their first stroke were hospitalized in the department of neurology of Tianjin Huanhu Hospital from 2015.6.1 to 2016.12.31. We recorded the personal histories of patients. The MMSE (mini-mental state examination), MoCA (Montreal Cognitive Assessment), HAMD (Hamilton Depression Scale), National Institutes of Health Stroke Scale (NIHSS) score, mRS (Modified Rankin Scale), BI (Barthel Index), PSQI (Pittsburgh Sleep Quality Index), ESS (Epworth Sleepiness Scale), Berlin questionnaire, and nocturnal TST (Total sleep time) were assessed before discharge, 3 months, 6 months, and 4 years (2019–2020) after stroke. Result Low sleep quality (OR 2.019, 95%CI 1.199–3.398, p = 0.008), nocturnal TST (<7 h) (OR 4.060, 95%CI 1.494–11.034, p = 0.006), nocturnal TST (>8 h) (OR 5.928, 95% CI 2.134–16.464, p = 0.001) were risk factors for poor neurological function recovery at 3 months after stroke. Nocturnal TST (<7 h) (OR 13.042, 95%-CI 2.576–66.027, p = 0.002) and nocturnal TST (>8 h) (OR 11.559, 95%-CI 2.108–63.390, p = 0.005) were risk factors for poor neurological function at 6 months after stroke. Nocturnal TST (<7 h) (OR 2.668, 95% CI 1.250–5.698, p = 0.011) and nocturnal TST (>8 h) (OR 2.516, 95% CI 1.080–5.861, p = 0.033) were risk factors for poor neurological function at 4 years after stroke. High risk of OSA (HR 1.582, 95%CI 1.244–2.012, p < 0.001) was a risk factor for all-cause death in patients followed up for 4 years after stroke. Conclusion Low sleep quality is associated with short-term poor neurological function after stroke. Unusual nocturnal TST (long or short) is associated with short-term or long-term poor neurological function after stroke. A high risk of OSA is associated with a higher risk of all-cause death after stroke.
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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".