Pre-stroke cognitive impairment and its impact on medication adherence
Bibliographic record
Abstract
Adherence to long-term medication is one of the most important components of effective therapy. Many factors have a substantial influence on medication adherence; a special role among them is played by cognitive impairment (CI). Objective : to identify whether poststroke patients have pre-stroke cognitive deficit and to assess its impact on adherence to long-term medication. Patients and methods . A total of 103 patients with acute ischemic stroke in the carotid system were examined. The mean age of the patients was 64.18±10.24 years. The Montreal Cognitive Assessment (MoCA) was applied to assess cognitive functions; the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) was used to determine the presence of pre-stroke cognitive decline. Data concerning vascular risk factors were collected for all the patients. Medication adherence was retrospectively evaluated using the Morisky–Green scale. Results and discussion . Our study showed that only 44.7% of patients were adherent to long-term medication before the stroke. Patients who were engaged in manual labor during their lives were significantly more poorly compliant. Chronic heart failure was also responsible for a reduction in medication adherence. Pre-stroke cognitive deficit was present in 53.4% of the examinees. Unlike patients with normal cognitive function, the majority of patients with pre-stroke CI were non-adherent to medication (28.1 and 71.9%, respectively). At the same time, the adherence to long-term medication depended on the severity of cognitive deficit. Conclusion . The results of the investigation suggest that CI has a considerable impact on adherence to long-term therapy. To improve primary stroke prevention, cognitive functions should be evaluated in all patients with vascular diseases who receive long-term drug treatment. When CI is identified, there is a need for targeted drug treatment and its proper monitoring.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".