Frequency and Predictors of Non-Compliance to Aspirin Therapy in Post Myocardial Infarction Patients
Bibliographic record
Abstract
BACKGROUND: Aspirin non-adherence or discontinuation is associated with an almost three-fold increase in risk of major adverse cardiac events. Compliance, commonly known as adherence, has been a major health care issue. Some studies have reported non-adherence rates to aspirin as high as fifty percent. The main objective of this study was to determine the frequency and predictors of non-compliance to aspirin in post myocardial infarction patients.METHODS: This cross sectional study was conducted over a period of 3 months from May 2015 to July 2015 at Civil Hospital, Karachi. All patients visiting Cardiology out-patient department (OPD) with previously diagnosed myocardial infarction were included in the study. Patients who were not prescribed aspirin or those with contraindication to aspirin therapy such as hemophiliacs and peptic ulcer disease patients, and those with memory problems were excluded from the study. A pre-coded questionnaire was presented to the selected sample of 456 patients. Compliance was assessed through self-report. Chi square test was used as the primary statistical test.RESULTS: Out of 456 patients, 39% (n=178) were non-compliant to aspirin therapy. The most common reported cause for non-compliance was the failure to remember taking the drug reported by 40.7% (n=72) of the people. The second most common cause was the lack of awareness of the importance of the drug and the possible side effects of not taking it 31.4% (n =56).CONCLUSION: It can be concluded that non-compliance to aspirin is a major problem present in Pakistan. With the number of cardiovascular deaths increasing around the globe and in Pakistan, it is vital that non-compliance to aspirin should be taken as a serious issue.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".