Methods for trials of interventions to enhance patient adherence to medication prescriptions, based on a systematic review of recent randomized trials
Bibliographic record
Abstract
Background: Low patient adherence to prescribed medications is very common and likely the most important barrier to the implementation of effective health care.Controlled trials of interventions to improve adherence have not found consistent success for any type of approach, but the research methods of these trials can lead to biased and imprecise testing, making it difficult to distinguish poor interventions from poor testing.Purpose: To outline key methodologic principles for testing adherence interventions and compare these with the methods used in recent randomized controlled trials from a systematic review.Methods: All recent trials included in an update of a Cochrane review of interventions to assist patients to adhere to prescribed medication were assessed for selection of participants, measurement of baseline adherence, data analysis according to baseline adherence, allocation to study groups, description of interventions, measures of medication adherence and clinical outcomes, and blinding.Results: Of 109 new trials included in the systematic review update, 51% measured baseline adherence, 5% specifically recruited non-adherent participants, and 10% reported their final results according to baseline adherence.Concealment of allocation to study groups was unclear in 65% of trials.Subjective measures of adherence were used in 48% of studies and 68% did not report on clinically important outcomes.Only 39% of studies reported on adverse effects of interventions and just 11% reported on incremental costs.We recommend remedies for these methodologic limitations.Conclusion: Recent trials of interventions to assist patients to take prescribed medications fail on key methodologic practices for fair and precise testing.This may be a major reason for the failure to identify effective ways to improve patient adherence and health care outcomes for self-administered treatments.Many of the failings are remediable.
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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 | 0.257 | 0.440 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.025 | 0.032 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".