Measures of Retention in HIV Care: A Study Within a Review
Bibliographic record
Abstract
People living with HIV (PLHIV) need lifelong medical care. However, retention in HIV care is not measured uniformly, making it challenging to compare or pool data. The objective of this study within a review (SWAR) is to describe the assortment of definitions used for retention in HIV care in randomized controlled trials (RCTs). We conducted a SWAR, drawing data from an overview of systematic reviews on interventions to improve the HIV care cascade. Ethics review was not required for this analysis of secondary data. We identified RCTs of interventions used to improve retention in care for PLHIV, including all age groups and extracted the definitions used and their characteristics. We identified 50 trials that measured retention published between 2007 and 2021 and provided 59 definitions for retention in care. The definitions consisted of nine different characteristics with follow-up time (n = 47), and clinical visits (n = 36) most used. The definitions of retention in HIV care are highly heterogeneous. In this study, we present the pros and cons of characteristics used to measure retention in HIV care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".