Nonadherence Increases the Risk of Hospitalization Among HIV-Infected Antiretroviral Naïve Patients Started on HAART
Bibliographic record
Abstract
BACKGROUND: Since the advent of highly active antiretroviral therapy (HAART), AIDS-related hospitalizations have decreased. The objective of this study was to assess the impact of adherence on hospitalization among antiretroviral-naïve HIV-infected persons initiating HAART. METHODS: Analysis was based on a cohort of individuals initiating HAART between 1996 and 2001. The primary outcome was hospitalization for one or more days. Survival methods were used to assess the impact of adherence on hospitalization. RESULTS: Of 1605 eligible participants, 672 (42%) were hospitalized for one or more days after initiating HAART. Median adherence levels were 92 (IQR: 58, 100) and 100 (IQR: 83, 100) among those ever and never hospitalized, respectively. After controlling for confounders, those with <95% adherence had 1.88 times (95% CI: 1.60, 2.21) higher risk for hospitalization. CONCLUSIONS: Suboptimal adherence among HIV-infected patients taking HAART predicts hospitalization. Identifying and addressing factors contributing to poor adherence early in treatment could improve patient care and lower hospitalization costs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".