Relation between sleep disorders and post-stroke cognitive impairment
Bibliographic record
Abstract
Objective: To investigate the effects of sleep disorders on post-stroke cognitive impairment (PSCI) and other factors affecting post-stroke cognitive impairment. Methods: A total of 1,542 first-ever stroke inpatients in department of neurology of Tianjin Huanhu Hospital from 2015.6.1 to 2016.12.31. We recorded the personal history of patients. The MMSE (mini-mental state examination), MoCA (Montreal Cognitive Assessment), HAMD (Hamilton Depression Scale), BI (Barthel index), mRS (Modified Rankin Scale), PSQI (Pittsburgh Sleep Quality Index), ESS (Epworth Sleepiness Scale), Berlin questionnaire, nocturnal TST (total sleep time) were assessed before discharge. All patients were followed up at 3 months, 6 months, and 4 years (2019-2020) after stroke. During follow-up, the above scales should be evaluated again to assess the sleep status and cognitive function of patients at that time. Results: = 0.002) were risk factors for cognitive impairment 4 years after stroke. The prevalence of cognitive impairment with TIA were 79.3% at admission, 68.1% at 3-months follow-up, 62.1% at 6-months follow-up and 52.2% at 4-year follow-up. Conclusion: Long or short nocturnal TST (<7 h or >8 h) was a risk factor for cognitive impairment after stroke (3 months, 6 months and 4 years). Poor sleep quality and sleepiness were shown to be risk factors for cognitive impairment at 4-year follow-up. Cognitive impairment was very common in patients with TIA.
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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) | 0.002 | 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".