Understanding adherence to medications in type 2 diabetes care and clinical trials to overcome barriers: a narrative review
Why this work is in the frame
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Bibliographic record
Abstract
AIM: To identify factors affecting adherence to medications in type 2 diabetes (T2D) care and clinical trials. BACKGROUND: Adherence to medication is associated with better patient outcomes, lower healthcare costs, and improved quality and robustness of trial data. In T2D, non-adherence to regimens may compromise glycemic, blood pressure and lipid control, which can, in turn, increase morbidity and mortality rates. DESIGN: A literature search was performed to identify studies reporting adherence to medications and highlighting specific adherence challenges/approaches in T2D. The search was limited to clinical trials, comparative studies or meta-analyses, reported in English with a freely available abstract. DATA SOURCE: MEDLINE (31 December 2008 to 31 December 2013). REVIEW METHODS: Studies not reporting adherence to medications or highlighting adherence challenges/approaches in T2D, presenting only self-reported adherence or including fewer than 100 patients were excluded. Eligible reports are discussed narratively. RESULTS: Factors identified as having a detrimental impact on adherence were smoking, depression and polypharmacy. Conversely, increased convenience (e.g. pen compared with vial and syringe; medication supplied by mail order vs. retail pharmacy) was associated with better patient adherence, as were interventions that increased patient motivation (e.g. individualized, nurse-led consultation) and education. CONCLUSIONS: Medication adherence is influenced by complex and multifactorial issues, which can include smoking, depression, polypharmacy, convenience of obtaining and administering the medication, patient motivation and education. We recommend simplifying treatment regimens, where possible, improving provider-patient communication, and providing support and education to increase medication adherence, with a view to improving patient outcomes and clinical trial data quality.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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