Evolving Profile and Determinants of Post-Stroke Cognitive Impairment in the 3rd Month among Kinshasa’s Survivors (Democratic Republic of the Congo)
Bibliographic record
Abstract
Background: Neurocognitive impairments are common among stroke survivors. Despite their negative impact on daily life, their evolving, and determinants are not fully known in our context. To determine evolving characteristics of post-stroke cognitive impairment in the 3rd month as well as determinants among Kinshasa’s adult survivors is the aim of this study. Methods: We sought to determine neurocognitive deficits in the 3rd month in a prospective single-group cohort study in 3 hospital centers in Kinshasa. Eighty-six adult stroke survivors with a neurological and neuroimaging computerized diagnosis of stroke were assessed using MOCA (Montreal Cognitive Assessment) in the first and the third months post-stroke. Results: Neurocognitive disorders ranged from 79.1% in the first month to 54.7% in the third month after stroke (with 4.7% with severe decline). Gender female [AOR = 86.3 (CI95%: 2.8 - 2643.7); p 0.01], Chronic hypertension ([AOR = 26.8 (CI95%: 2.55 - 282.55); p 0.01]), the pathological lipid profile [AOR = 8.7 (CI95%: 1.10 - 68.82); p = 0.04] and worse MOCA score at the first month ([AOR = 41.2 (CI95%: 8.13 - 2134.81); p = 0.021]) were identified as worse predictors of cognitive impairments at the third month post-stroke. Conclusion: Post-stroke cognitive impairment is common and decreases in the 3rd month post-stroke. Chronic hypertension, gender, lipid profile, and the first month MOCA score are predictors of worse cognitive performance in Kinshasa survivors. These findings suggested the role of early management in improving cognition and the control of stroke risk factors.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".