Factors associated with cognitive impairment at 3, 6, and 12 months after the first stroke among Lebanese survivors
Bibliographic record
Abstract
INTRODUCTION: This study aimed to calculate the rate of post-stroke cognitive impairment (PSCI) by evaluating the cognitive domains among Lebanese stroke survivors at 3, 6, and 12 months post-stroke, and to identify the contributing factors including pre- and post-stroke related factors. METHODS: A multicenter longitudinal prospective study was conducted in 10 hospitals from Beirut and Mount Lebanon for a 15-month period. Mini-Mental State Examination (MMSE), modified Rankin Scale (mRS), Short Form Health Survey (SF12), National Institutes of Health Stroke Scale (NIHSS), and Hospital Anxiety and Depression Scale (HADS) were used to assess cognitive function, disability degree, Quality of Life (QoL), stroke severity, and levels of anxiety and depression, respectively. Then, univariate and multivariable analyses were performed to identify the predictors of PSCI. RESULTS: Low MMSE scores were found among survivors during the first 3 months post-stroke (74.8%) of whom 53.7% presented with an MMSE ≤ 17, followed by 46.7% in the 6 months, and 37.6% at 12 months post-stroke. Follow-up comparisons showed a significant increase of MMSE scores over time (p < .001), indicating a 37% improvement of the cognitive function over time. The most affected cognitive domain was the attention and concentration at the three time points. Independent factors that were positively associated with low MMSE scores were as follows: sedentary behavior ≥ 12 h/day (AOR = 3.062, p = .033), involvement of the left hemisphere (AOR = 2.710, p = .006), HADS ≥ 11 (AOR = 2.536, p = .049), and high NIHSS scores (AOR = 3, p = .009). Age was the main predictor in the three time periods (AOR ≈ 3, p < .05). Inversely, female gender (AOR = 0.09, p = .027), high educational level (AOR = 0.2, p < .02), employment post-stroke (AOR = 0.3, p = .023), high Physical Component Summary (PCS) of Quality of Life (QoL) (AOR = 0.8, p < .001), and the use of anti-diabetic treatment post-stroke (AOR = 0.17, p = .016) improved MMSE scores to > 23. CONCLUSION: The risk of PSCI among Lebanese stroke survivors was high especially in the acute phase, depending on various determinants. Health care providers are invited to implement an emergency rehabilitation program for an appropriate successful management of the risk factors in order to reduce stroke burden and to improve overall cognitive performance.
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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 | 0.001 | 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".