Virologic and Immunologic Effectiveness of Tipranavir/Ritonavir (TPV/r)- Versus Darunavir/Ritonavir (DRV/r)-Based Regimens in Clinical Practice
Bibliographic record
Abstract
BACKGROUND: Although both tipranavir and darunavir are important options for the management of patients with multidrug resistant HIV, there are at present no studies comparing the effectiveness and safety of these 2 antiretroviral drugs in this population of patients. OBJECTIVE: To compare the effectiveness and safety of ritonavir (TPV/r)- and darunavir/ritonavir (DRV/ r)-based therapies in treatment-experienced patients (n = 38 and 47, respectively). METHODS: Multicenter, retrospective cohort study. RESULTS: The median baseline viral load and CD4 count were 4.7 copies/mL (interquartile range [IQR] 4.3, 5.2) and 168 cells/mm( 3) (IQR 80, 252) for TPV/r patients and 4.7 copies/mL (IQR 3.7, 5.1) and 171 cells/mm(3) (IQR 92, 290) for DRV/r patients. The median number of years on antiretroviral therapy (ART) prior to starting DRV/r or TPV/r were 12.7 (10.2-15.5) and 10.5 (8.4-12.6), respectively (P < .01). Current raltegravir (RAL) use (odds ratio [OR] 5.53, 95% CI 1.08-28.34) was significantly associated with virologic suppression at week 24 in multivariable logistic regression models, whereas the use of TPV/r was not significantly associated with virologic suppression compared to DRV/r (OR 0.93, 95% CI 0.27-3.18, P = .91). CONCLUSION: No significant difference was observed between DRV/r and TPV/r in terms of virologic suppression.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".