Bibliographic record
Abstract
Abstract: Advances in drug therapy have resulted in efficacious treatments being available; however, the benefit may be lost if prescribed medications are not taken properly. Unfortunately, poor medication adherence is common and widespread, affecting all age groups and disease conditions. Adherence is a factor in health outcomes of pharmacotherapy with possible failure to achieve therapeutic goals and worsening of illness. Higher health care costs may result from more frequent physician and emergency department visits and increased hospitalization rates. The cost of medications may play a role in whether patients do or do not take their medication with increased cost sharing leading to poorer adherence with prescription drugs. Given the possible adverse consequences of nonadherence, interventions to improve medication-taking behavior are encouraged although not consistently successful. Surprisingly, there is relatively little information on the cost-effectiveness of these interventions and more methodologically sound research is needed in this area. Alternative strategies that have been proposed are value-based insurance design and the use of financial incentives, although the former has not been widely accepted, and the latter is ethically controversial. This article reviews some of the main issues with regards to adherence with drug therapy including some of the cost implications of less than optimal medication adherence. Keywords: adherence, medication, cost
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".