Deprescribing as a Way to Reduce Inappropriate Use of Drugs for Overactive Bladder in Primary Care (DROP): Protocol for a Cluster Randomized Controlled Trial With an Embedded Explanatory Sequential Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Potentially inappropriate medication remains a significant concern in general practices, particularly in the context of overactive bladder (OAB) treatment for individuals aged 65 years and older. This study focuses on the exploration of alternative options for treating OAB and the deprescribing of anticholinergic drugs commonly used in OAB. The research aims to comprehensively evaluate the efficiency of deprescribing through a mixed methods approach, combining quantitative assessment and qualitative exploration of perceptions, experiences, and potential barriers among patients and health care personnel. OBJECTIVE: This study aims to evaluate the efficiency and safety of the intervention in which health care staff in primary care encourage patients to participate in deprescribing their drugs for OAB. In addition, we aim to identify factors contributing to or obstructing the deprescribing process that will drive more informed decisions in the field of deprescribing and support effective and safe treatment of patients. METHODS: The drugs for overactive bladder in primary care (DROP) study uses a rigorous research design, using a randomized controlled trial (RCT) with an embedded sequential explanatory mixed methods approach. All general practices within the North Denmark Region will be paired based on the number of general practitioners (GPs) and urban or rural locations. The matched pairs will be randomized into intervention and control groups. The intervention group will receive an algorithm designed to guide the deprescribing of drugs for OAB, promoting appropriate medication use. Quantitative data will be collected from the RCT including data from Danish registries for prescription analysis. Qualitative data will be obtained through interviews and focus groups with GPs, staff members, and patients. Finally, the quantitative and qualitative findings are merged to understand deprescribing for OAB comprehensively. This integrated approach enhances insights and supports future intervention improvement. RESULTS: The DROP study is currently in progress, with randomization of general practices underway. While they have not been invited to participate yet, they will be. The inclusion of GP practices is scheduled from December 2023 to April 2024. The follow-up period for each patient is 6 months. Results will be analyzed through an intention-to-treat analysis for the RCT and a thematic analysis for the qualitative component. Quantitative outcomes will focus on changes in prescriptions and symptoms, while the qualitative analysis will explore experiences and perceptions. CONCLUSIONS: The DROP study aims to provide an evidence-based intervention in primary care that ensures the deprescription of drugs for OAB when there is an unfavorable risk-benefit profile. The DROP study's contribution lies in generating evidence for deprescribing practices and influencing best practices in health care. TRIAL REGISTRATION: ClinicalTrials.gov NCT06110975; https://clinicaltrials.gov/study/NCT06110975. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56277.
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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 | 0.053 | 0.052 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.009 |
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".