Primary Medication Non-Adherence after Discharge from a General Internal Medicine Service
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Medication non-adherence frequently leads to suboptimal patient outcomes. Primary non-adherence, which occurs when a patient does not fill an initial prescription, is particularly important at the time of hospital discharge because new medications are often being prescribed to treat an illness rather than for prevention. METHODS: We studied older adults consecutively discharged from a general internal medicine service at a large urban teaching hospital to determine the prevalence of primary non-adherence and identify characteristics associated with primary non-adherence. We reviewed electronic prescriptions, electronic discharge summaries and pharmacy dispensing data from April to August 2010 for drugs listed on the public formulary. Primary non-adherence was defined as failure to fill one or more new prescriptions after hospital discharge. In addition to descriptive analyses, we developed a logistical regression model to identify patient characteristics associated with primary non-adherence. RESULTS: There were 493 patients eligible for inclusion in our study, 232 of whom were prescribed new medications. In total, 66 (28%) exhibited primary non-adherence at 7 days after discharge and 55 (24%) at 30 days after discharge. Examples of medications to which patients were non-adherent included antibiotics, drugs for the management of coronary artery disease (e.g. beta-blockers, statins), heart failure (e.g. beta-blockers, angiotensin converting enzyme inhibitors, furosemide), stroke (e.g. statins, clopidogrel), diabetes (e.g. insulin), and chronic obstructive pulmonary disease (e.g. long-acting bronchodilators, prednisone). Discharge to a nursing home was associated with an increased risk of primary non-adherence (OR 2.25, 95% CI 1.01-4.95). CONCLUSIONS: Primary non-adherence after medications are newly prescribed during a hospitalization is common, and was more likely to occur in patients discharged to a nursing home.
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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) | 0.028 | 0.011 |
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