A Patient Decision Aid (i.ARTs) to Facilitate Women’s Choice Between Oral and Long-Acting Injectable Antiretroviral Treatment for HIV: Protocols for its Development and Randomized Controlled Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Many women with HIV (WWH) have suboptimal adherence to oral antiretroviral therapy (ART) due to multilevel barriers to HIV care access and retention. A long-acting injectable (LAI) version of ART was approved by the US Food and Drug Administration in January 2021 and has the potential to overcome many of these barriers by eliminating the need for daily pill taking. However, it may not be optimal for all WWH. It is critical to develop tools that facilitate patient-provider shared decision making about oral versus LAI ART modalities to promote women's adherence and long-term HIV outcomes. OBJECTIVE: This study will develop and pilot test a web-based patient decision aid called i.ART+support (i.ARTs). This decision aid aims to support shared decision making between WWH and their providers, and help women choose between oral and LAI HIV treatment. METHODS: The study will occur in 3 phases. In phase 1, we will utilize a mixed methods approach to collect data from WWH and medical and social service providers to inform i.ARTs content. During phase 2, we will conduct focus groups with WWH and providers to refine i.ARTs content and develop the web-based decision aid. In phase 3, i.ARTs will be tested in a randomized controlled trial with 180 women in Miami, Florida, and assessed for feasibility, usability, and acceptability, as well as to evaluate the associations between receiving i.ARTs and viral suppression, ART pharmacy refills, and clinic attendance. RESULTS: This study was funded in March 2021. Columbia University's IRB approved the study protocols (approval number IRB-AAAT5314). Protocols for phase 1 interviews have been developed and interviews with service providers started in September 2021. We will apply for Clinicaltrials.gov registration prior to phase 3, which is when our first participant will be enrolled in the randomized controlled trial. This is anticipated to occur in April 2023. CONCLUSIONS: This study is the first to develop a web-based patient decision aid to support WWH choices between oral and LAI ART. Its strengths include the incorporation of both patient and provider perspectives, a mixed methods design, and implementation in a real-world clinical setting. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35646.
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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 | 0.046 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 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; 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".