Differential impact of adherence on long-term treatment response among naive HIV-infected individuals
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To examine the long-term impact of adherence on virologic, immunologic, and dual response stratified by type of HAART regimen in treatment-naive patients starting HAART in British Columbia, Canada; and to assess the degree of virologic and immunologic response associated with emergence of drug resistance, progression to AIDS, and mortality. METHODS: Eligible participants initiated HAART between 1 January 2000 and 30 November 2004, were followed until 30 November 2005, and had at least 2 years of follow-up. Virologic and immunologic responses were dichotomized at their median values. Virologic response was defined as at least 65% of follow-up time with plasma viral load (pVL) of less than 50 copies/ml. Immunologic response was defined as a CD4 cell count increase of at least 145 cells/microl. Adherence measures were based on prescription refill compliance. Proportional odds models and logistic regression were used to address our objectives. RESULTS: The distribution of patient responses was 394 (44.9%) for CD4+/pVL+ (best), 350 (39.9%) for CD4-/pVL+ or CD4+/pVL- (incomplete), and 134 (15.3%) for CD4-/pVL- (worst). We found a positive correlation between adherence and virologic and immunologic responses (P < 0.01). Having worst compared with best response (reference group) was associated with higher odds of mortality (odds ratio: 6.09; 95% confidence interval: 2.57-14.42) and emergence of drug resistance (odds ratio: 10.56; 95% confidence interval: 5.93-18.81) even after adjusting for adherence and HAART regimen. CONCLUSION: Patients not attaining the best virologic and immunologic responses are at a high risk for emergence of drug resistance and mortality, and these responses are highly dependent on the adherence level and initial HAART regimen. Patients on protease inhibitor-single did worse no matter the adherence level.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it