Abstract TP155: Cognitive Impairment in Mild Stroke
Bibliographic record
Abstract
Background: Cognitive symptoms are easily overlooked despite its effect on quality of life after stroke. In patients with minimal physical residual symptoms, the true burden of cognitive impairment is unknown. Objective: To evaluate the risk factors and burden associated with cognitive impairment after mild stroke. Methods: A retrospective observational cohort study of 56 patients (51 ischemic, 5 hemorrhagic) evaluated at the stroke clinic between July 2016 and June 2017 was performed. Patients completed a questionnaire including demographics and history including previously known cognitive impairment. Cognition was evaluated with the Montreal Cognitive Assessment Score (MOCA). Results: The median age of the group studied was 61.5 years. Sixty-six percent (66.1%) patients were men and 76.8% Caucasian. The median discharge NIH score was 1. This study group did not have a previous diagnosis of dementia. About 4% (n=2) of patients presented to clinic <6 weeks, 71.4% (n=40) 6-12 weeks, 17.9% (n=10) 12-24weeks and 7.1% (n=4) >24 weeks from discharge. Cognitive assessment at the stroke clinic after discharge identified 19.6% patients with mild impairment, 16.1% with moderate impairment and 14.3% with dementia. MOCA scores were lower in those greater than 65 years of age ( p =0.022), living in facilities post-stroke ( p =0.007), have history of stroke and diabetes mellitus ( p =0.011, 0.019). Education level did not show significant difference in MOCA scores ( p =0.283). Those who were employed prior to their stroke have a higher MOCA score compared to those who were unemployed ( p =0.001). Of those previously working, 50% were able to return to work and this group had higher MOCA scores ( p =0.011). Of those who stopped driving, 19.6% was due to cognitive concerns. Conclusion: In our study, about 50% of the mild stroke patients were found to have some degree of cognitive impairment. Factors that may suggest a higher risk for cognitive impairment after stroke include those age greater than 65, history of stroke and diabetes, unemployment and living in a facility. Post-stroke cognitive impairment was found to be associated with inability to return to work and drive. Since cognitive impairment can impact life quality, screening even in mild stroke could be beneficial.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".