Re-inventing adherence: toward a patient-centered model of care for drug-resistant tuberculosis and HIV
Bibliographic record
Abstract
BACKGROUND: Despite renewed focus on molecular tuberculosis (TB) diagnostics and new antimycobacterial agents, treatment outcomes for patients co-infected with drug-resistant TB and human immunodeficiency virus (HIV) remain dismal, in part due to lack of focus on medication adherence as part of a patient-centered continuum of care. OBJECTIVE: To review current barriers to drug-resistant TB-HIV treatment and propose an alternative model to conventional approaches to treatment support. DISCUSSION: Current national TB control programs rely heavily on directly observed therapy (DOT) as the centerpiece of treatment delivery and adherence support. Medication adherence and care for drug-resistant TB-HIV could be improved by fully implementing team-based patient-centered care, empowering patients through counseling and support, maintaining a rights-based approach while acknowledging the responsibility of health care systems in providing comprehensive care, and prioritizing critical research gaps. CONCLUSION: It is time to re-invent our understanding of adherence in drug-resistant TB and HIV by focusing attention on the complex clinical, behavioral, social, and structural needs of affected patients and communities.
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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 | 0.050 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".