Beliefs about medicines in dialysis patients and after renal transplantation
Bibliographic record
Abstract
INTRODUCTION: Patients with end-stage renal disease are burdened by a complex medication regimen, but little is known about the belief about medicine among dialysis- and renal transplant (RTX) patients. Patients' beliefs about medicines may influence drug adherence and thereby affect morbidity and mortality. The aim of the present study was to assess the beliefs about medicine in dialysis as well as after RTX. METHODS: In a prospective study, 301 dialysis patients were followed for up to 5.5 years during which time 142 had been transplanted. Out of the transplanted patients, 110 were eligible for inclusion. The Beliefs about Medicine Questionnaire (BMQ) was used to assess the beliefs in dialysis and after transplantation. BMQ in dialysis was also compared to that of the general Norwegian population (n = 426). Multiple linear regression analyses were performed with BMQ subscales as dependent variables and sociodemographic and clinical data as independent variables. FINDINGS: Median age in dialysis was 62 (IQR 50-73) years, 66.1% were male and 80.7% were treated with hemodialysis. When in dialysis, 98.2% strongly believed their medications were necessary, while 34.4% reported strong concerns. Furthermore, 17.3% believed their medications to be harmful and 38.6% believed that doctors overprescribed medicines. The Necessity-concern differential had a positive score in 92.6% of the patients. Follow-up time was 55 (IQR 50-59) months. After transplantation, there was an increase in the patient-reported necessity of medication (21.9 ± 2.7 vs. 23.8 ± 1.9, P < 0.001) compared to while in dialysis. Correlations were found between patient beliefs and education, age, and depression. DISCUSSION: Although positive beliefs about medicines increase after transplantation, concerns are high in both dialysis and after RTX. Implementing the BMQ routinely in the clinical evaluation of dialysis- and RTX patients may help to identify patients with increased risk for medical nonadherence.
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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) | 0.003 | 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".