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 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 | 0.004 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".