Relationship Between Anticoagulant Medication Adherence and Satisfaction in Patients With Stroke
Bibliographic record
Abstract
AIM: The aim of this study was to investigate the accuracy of the self-reported measure of adherence and the relation between adherence to warfarin use, demographic and clinical variables, and the satisfaction with the treatment in patients affected by stroke. METHODS: This is a correlational, quantitative, and cross-sectional study, carried out in the outpatient clinics of a public university hospital from October 2017 to April 2018. Sociodemographic and clinical data were collected through interviews and hospital charts, as well as by applying the Measurement of Treatment Adherence (MTA) and the Duke Anticoagulation Satisfaction Scale, in their Brazilian versions. Results of the international normalized ratio (INR) were collected. Measurements of accuracy of the MTA scale were calculated in relation to the INR classification. RESULTS: Of 99 patients (55.6% male with a mean age of 58.6 years), 57.6% presented with therapeutic INR values and 75.8% of the patients were adherent to the oral anticoagulant therapy according to the MTA. The accuracy analysis of the measurement provided by the MTA scale in relation to the INR classification showed a sensitivity of 77.2% and a specificity of 26.2%. The patients' satisfaction with the treatment was high. The Duke Anticoagulation Satisfaction Scale had an average total score of 46.4, with the dimension impact in the field having the highest score (20.3). CONCLUSION: Stroke patients were adherent and satisfied with the oral anticoagulant therapy. The MTA had good sensitivity and poor specificity. Sociodemographic and clinical characteristics identified were not associated with adherence and satisfaction with treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".