Early cognitive dysfunction after stroke and related risk factors in the high-altitude and multi-ethnic region of Qinghai, China: A multi-center cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Cognitive impairment is a common outcome of stroke, but there is limited evidence regarding its prevalence at high altitude, especially within the context of specific ethnic groups or lifestyle habits. This prospective exploratory study investigated early cognitive impairment after stroke in Qinghai Province, 3000 m above sea level. METHODS: Patients with acute stroke (n = 1047) were enrolled from 3 hospitals in Qinghai Province. Cognitive performance was measured by Montreal Cognitive Assessment (MoCA) scores within 5 days of stroke symptom onset; MoCA < 26 defined impairment. Patient data included demographics, education, vascular risk factors, diet, and activities of daily living rated by Barthel index. RESULTS: Cognitive impairment within 5 days of stroke symptom onset affected 77.65% of these patients. The factors independently associated with early cognitive impairment were: older age (mean difference [MD]: -4.857, 95% confidence interval [CI]: 6.685-3.030, P < 0.001); female gender (odds ratio [OR]: 1.674, 95% CI: 1.212-2.313, P = 0.002); and a diet containing yak butter (OR: 1.587, 95% CI: 1.247-2.021, P < 0.001). Progressively lesser odds were accounted to beef (Yak) and mutton consumption (OR: 0.804, 95% CI: 0.655-0.987, P = 0.037); fruit (OR: 0.792, 95% CI: 0.672-0.933, P = 0.005); status as an immigrant (OR: 0.666, 95% CI: 0.445-0.996, P = 0.048); education (OR: 0.514, 95% CI: 0.400-0.660, P < 0.001); and multiple daily leisure activities (OR: 0.999, 95% CI: 0.999-0.999, P < 0.001). CONCLUSION: Persons in Qinghai province who experience stroke are likely to show signs of early cognitive dysfunction. Preventive modifiable features include diet and daily activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".