Self-Reported Morisky Score for Identifying Nonadherence with Cardiovascular Medications
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The Morisky medication adherence scale is a commonly used adherence screening tool. It is composed of 4 yes/no questions about past medication use patterns and is thus quick and simple to use during drug history interviews. OBJECTIVE: To evaluate the use of the self-reported Morisky score as a screening tool for identifying patients who have been nonadherent with chronic cardiovascular medications. METHODS: Patients who had taken an angiotensin-converting enzyme inhibitor or lipid-lowering agent for at least 3 consecutive months were interviewed using a structured questionnaire including the Morisky scale. Nonadherence was defined as taking < 80% of chronic cardiovascular medications based on prescription refill data over the previous 14 months. RESULTS: Forty-nine of 377 (13%) patients were categorized as nonadherent; however, only 12 (3%) patients had Morisky scores suggesting a high likelihood of nonadherence (3 or 4). While the Morisky score was a significant independent predictor of nonadherence by multivariate analysis, there was no threshold score or individual question that yielded concurrent high sensitivity and positive predictive values (PPVs) for identifying nonadherent patients. The internal consistency of the questions was low (alpha 0.32), as were item-to-total score correlations, suggesting that the individual questions were not measuring the same attribute. CONCLUSIONS: Using the Morisky scale to identify patients who have been nonadherent with chronic cardiovascular medications may be reasonable in some settings; however, the threshold score would have to be chosen based on a trade-off between sensitivity and PPV. These results were likely influenced by the low rate of nonadherence in this cohort. Rewording the questions, increasing the number of questions, and the use of graded response options may improve consistency.
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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 | 0.000 |
| 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 it