Discordant Immunologic and Virologic Responses to Highly Active Antiretroviral Therapy Are Associated With Increased Mortality and Poor Adherence to Therapy
Bibliographic record
Abstract
OBJECTIVE: To examine the independent association of discordant virologic and immunologic responses to highly active antiretroviral therapy (HAART) with mortality. METHODS: A population-based study of 1527 treatment-naive individuals initiating HAART used Cox proportional hazards modeling to determine the independent association of treatment response at 3 to 9 months with nonaccidental mortality. Logistic regression was used to examine associations with discordant responses. RESULTS: Viral load (VL)/CD4 discordant responses were seen in 235 (15.4%) of subjects, and VL/CD4 responses were seen in 179 (11.7%) of subjects. In adjusted Cox regression models, discordant responses were found to be independently associated with an increased risk of mortality (VL/CD4: relative hazard [RH] = 1.87, 95% confidence interval [CI]: 1.15 to 3.04; VL/CD4: RH = 2.47, 95% CI: 1.54 to 3.95). VL/CD4 discordance was found to be associated with increasing age, baseline HIV RNA load <100,000 copies/mL, baseline CD4 counts <50 cells/muL, the use of lamivudine (3TC)/zidovudine (ZDV), and poor adherence to therapy. VL/CD4 discordance was associated with younger age; injection drug use; baseline HIV RNA load >100,000 copies/mL; the use of 3TC/ZDV, didanosine (ddI)/3TC, or ddI/stavudine; and poor adherence to therapy. CONCLUSION: Discordant responses are independently associated with an increased risk of mortality and are, in turn, associated with poor adherence to therapy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".