Analysis of factors affecting adherence to treatment in post-stroke patients
Bibliographic record
Abstract
Introduction. The effectiveness of secondary stroke prevention depends not only on the prescribed medications but also on patients’ compliance with doctors’ recommendations in general. Adherence to therapy among post-stroke patients remains insufficient. This is due to factors that negatively affect compliance with medical recommendations. Among those factors, post-stroke cognitive impairment deserves particular attention. Study aim – to identify the main factors that determine adherence to long-term therapy in patients after stroke and to assess the impact of post-stroke cognitive impairment on compliance with medical recommendations. Materials and methods. A total of 56 patients (mean age 64.67±10.19 years), who experienced a hemispheric ischaemic stroke 6 months ago, were examined. Cognitive function was evaluated using the Montreal Cognitive Assessment tool, the battery of tests to assess frontal dysfunction, drawing and clock copying tests, and tests of phonetic and semantic speech activity. Adherence to long-term therapy after stroke was determined using the Morisky–Green test. We also studied the role of sociodemographic and vascular risk factors, that determine treatment adherence. Results. More than half (51.8%) of post-stroke patients did not comply with medical recommendations. The main barriers to optimal adherence were the male gender, engagement in physical labour throughout life, and the presence of left ventricular hypertrophy, chronic heart failure or bad health habits. The presence of post-stroke cognitive impairment had a negative impact on the adherence to medical recommendations (r=0.49; p<0.001). The results of the survey showed that regular visits to medical specialists were one of the main requirements for maintaining optimal adherence to treatment. Most patients (59.6%) thought that forgetfulness is a key factor affecting adherence to therapy. Summary. Treatment adherence should be evaluated in all patients after stroke, especially in those with post-stroke cognitive impairment. The identification and correction of ‘modifiable’ risk factors is a way to increase adherence to treatment
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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) | 0.001 | 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 | 0.000 |
| 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".