Baseline medication adherence and blood pressure in a 24‐month longitudinal hypertension study
Bibliographic record
Abstract
AIM AND OBJECTIVES: We sought to identify the feasibility and predictive validity of an easy and quick self-reported measure of medication adherence and to identify characteristics of people with hypertension that may warrant increase attentiveness by nurses to address hypertensive self-management needs. BACKGROUND: Current control rates of hypertension are approximately 50%. Effective blood pressure control can be achieved in most people with hypertension through antihypertensive medication. However, hypertension control can only be achieved if the patient is adherent with their medication regimen. Patients who are non-adherent may be in need of additional intervention. DESIGN: This secondary analysis evaluated the systolic blood pressure of patients who received usual hypertension management across 24 months at six-month intervals. METHODS: A longitudinal study of 159 hypertensive patients in two primary care clinics. RESULTS: In a sample of 159 patients receiving care in a primary care facility, baseline medication non-adherence was associated with a 6·3 mmHg increase in systolic blood pressure (p < 0·05) at baseline, a 8·4 mmHg increase in systolic blood pressure (p < 0·05) at 12 months and a 7·5 increase in systolic blood pressure at 24 months (p < 0·05) compared with adherent patients, respectively. Results also indicate a significant increase in systolic blood pressure across 24 months among people who identified as minority and of low financial status. CONCLUSIONS: Non-adherence with antihypertensive medication at baseline was predictive of increased systolic blood pressure up to 24 months postbaseline. RELEVANCE TO CLINICAL PRACTICE: This study demonstrates the use of an easy-to-use questionnaire to identify patients who are non-adherent. We recommend assessing medication adherence to identify patients who are non-adherent with their anti-hypertensive medication and to be especially vigilant with patients who are minority or are considered low income.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".