Medication regimen complexity and medication adherence in elderly patients with chronic kidney disease
Bibliographic record
Abstract
INTRODUCTION: Elderly patients with chronic kidney disease (CKD) stage 5 with or without dialysis treatment usually have concomitant comorbidities, which often result in multiple pharmacological therapies. This study aimed to identify factors associated with medication complexity and medication adherence, as well as the association between medication complexity and medication adherence, in elderly patients with CKD. METHODS: ) recruited from three Norwegian hospitals. Most of the patients were receiving either hemodialysis or peritoneal dialysis. We used the Medication Regimen Complexity Index (MRCI) to assess the complexity of medication regimens, and the eight-item Morisky Medication Adherence Scale (MMAS-8) to assess medication adherence. Factors associated with the MRCI and MMAS-8 score were determined using either multivariable linear or ordinal logistic regression analysis. FINDINGS: In total, 157 patients aged 76 ± 7.2 years (mean ± SD) were included in the analysis. Their overall MRCI score was 22.8 ± 7.7. In multivariable linear regression analyses, female sex (P = 0.044), Charlson Comorbidity Index of 4 or 5 (P = 0.029) and using several categories of phosphate binders (P < 0.001 to 0.04) were associated with the MRCI. Moderate or high adherence (MMAS-8 score ≥ 6) was demonstrated by 83% of the patients. The multivariable logistic regression analyses found no association of medication complexity, age or other variables with medication adherence as assessed using the MMAS-8. DISCUSSION: Female sex, comorbidity and use of phosphate binders were associated with more-complex medication regimens in this population. No association was found between medication regimen complexity, phosphate binders or age and medication adherence. These findings are based on a homogeneous elderly group, and so future studies should test if they can be generalized to patients of all ages with CKD.
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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 | 0.000 | 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) | 0.004 | 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".