A Randomized Controlled Trial Protocol to Evaluate the Effectiveness of an Integrated Care Management Approach to Improve Adherence Among HIV-Infected Patients in Routine Clinical Care: Rationale and Design
Bibliographic record
Abstract
BACKGROUND: Adherence to antiretroviral medications is a key determinant of clinical outcomes. Many adherence intervention trials investigated the effects of time-intensive or costly interventions that are not feasible in most clinical care settings. OBJECTIVE: We set out to evaluate a collaborative care approach as a feasible intervention applicable to patients in clinical care including those with mental illness and/or substance use issues. METHODS: We developed a randomized controlled trial (RCT) investigating an integrated, clinic-based care management approach to improve clinical outcomes that could be integrated into the clinical care setting. This is based on the routine integration and systematic follow-up of a clinical assessment of patient-reported outcomes targeting adherence, depression, and substance use, and adapts previously developed and tested care management approaches. The primary health coach or care management role is provided by clinic case managers allowing the intervention to be generalized to other human immunodeficiency virus (HIV) clinics that have case managers. We used a stepped-care approach to target interventions to those at greatest need who are most likely to benefit rather than to everyone to maintain feasibility in a busy clinical care setting. RESULTS: The National Institutes of Health funded this study and had no role in study design, data collection, or decisions regarding whether or not to submit manuscripts for publication. This trial is currently underway, enrollment was completed in 2015, and follow-up time still accruing. First results are expected to be ready for publication in early 2017. DISCUSSION: This paper describes the protocol for an ongoing clinical trial including the design and the rationale for key methodological decisions. There is a need to identify best practices for implementing evidence-based collaborative care models that are effective and feasible in clinical care. Adherence efficacy trials have not led to sufficient improvements, and there remains little guidance regarding how adherence interventions should be implemented into clinical care. By focusing on improving adherence within care settings using existing staff, routine assessment of key domains, such as depression, adherence, and substance use, and feasible interventions, we propose to evaluate this innovative way to improve clinical outcomes. TRIAL REGISTRATION: Clinicaltrials.gov NCT01505660; http://clinicaltrials.gov/ct2/show/NCT01505660 (Archived by WebCite at http://www.webcitation/ 6ktOq6Xj7).
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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 | 0.081 | 0.076 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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".