Electronic Dose Monitoring Identifies a High-Risk Subpopulation in the Treatment of Drug-resistant Tuberculosis and Human Immunodeficiency Virus
Bibliographic record
Abstract
BACKGROUND: In generalized drug-resistant tuberculosis (DR-TB) human immunodeficiency virus (HIV) epidemics, identifying subpopulations at high risk for treatment failure and loss to care is critically important to improve treatment outcomes and prevent amplification of drug resistance. We hypothesized that an electronic dose-monitoring (EDM) device could empirically identify adherence-challenged patients and that a mixed-methods approach would characterize treatment challenges. METHODS: A prospective study of patients with DR-TB HIV on antiretroviral therapy (ART) initiating bedaquiline-containing regimens in KwaZulu-Natal, South Africa. Separate EDM devices measured adherence for bedaquiline and ART. Patients with low adherence (<85%) to both bedaquiline and ART were identified as high risk for poor outcomes. Baseline survey, study visit notes, and focus group discussions characterized treatment challenges. RESULTS: From December 2016-February 2018, 32 of 198 (16%) enrolled patients with DR-TB HIV were identified as dual-adherence challenged. In a multivariate model including baseline characteristics, only receiving a disability grant was significantly associated with dual nonadherence at 6 months. Mixed-methods identified treatment barriers including alcohol abuse, family conflicts, and mental health issues. Compared with adherent patients, dual-adherence-challenged patients struggled to prioritize treatment and lacked support, and dual-adherence-challenged patients experienced higher rates of detectable HIV viral load and mortality than more adherent patients. CONCLUSIONS: EDM empirically identified a subpopulation of patients with DR-TB HIV with dual-adherence challenges early in treatment. Mixed-methods revealed intense psychosocial, behavioral, and structural barriers to care in this subpopulation. Our data support developing differential, patient-centered, adherence support interventions focused on psychosocial and structural challenges for subpopulations of at-risk DR-TB HIV patients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".