Long-Term Health Care Interruptions Among HIV-Positive Patients in Uganda
Bibliographic record
Abstract
BACKGROUND: Retaining patients in clinical care is necessary to ensure successful antiretroviral treatment (ART) outcomes. Among patients who discontinue care, some reenter care at a later stage, whereas others are or will be lost from follow-up. We examined risk factors for health care interruptions and loss to follow-up within a cohort receiving ART in Uganda. METHODS: Using a large hospital cohort providing free universal ART and HIV clinical care, we assessed characteristics and risk factors for treatment interruptions, defined as a 12-month absence from care at Mildmay, and loss to follow-up, defined as absence from care greater than 12 months without reengagement in care at Mildmay. We included patients aged 14 years and above. We assessed these outcomes over time using Kaplan-Meier analysis and multivariable regression. RESULTS: Of 6970 eligible patients, 784 (11.2%) had a health care interruption of at least 12 months and 217 (3.1%) were lost to follow-up. Patients experiencing health care interruptions had higher baseline CD4 T-cell counts at ART initiation, defined as ≥ 250 cells per cubic millimeter [odds ratio (OR): 1.29, 95% confidence intervals (CI): 1.11 to 1.50], and lower levels of education (OR: 1.32, 95% CI: 1.09 to 1.61). Adolescents were much more likely to be lost to follow-up (OR: 3.11, 95% CI: 2.23 to 4.34). In contrast, having a partner (OR: 0.22, 95% CI: 0.16 to 0.31) or being sexually active at baseline (OR: 0.40, 95% CI: 0.28 to 0.55) was protective of loss to follow-up. CONCLUSIONS: Within this cohort, long periods of unsupervised health care interruptions were common.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".