A Systematic Review of the Use of Atazanavir in Women Infected with HIV-1
Bibliographic record
Abstract
BACKGROUND: Despite increasing numbers of women with HIV worldwide, females are under-represented in clinical trials of antiretrovirals and literature addressing gender differences in clinical outcomes, treatment discontinuation, adverse events and adherence are limited. Most recommendations specific to women in current guidelines relate to pregnant women or women wishing to become pregnant. The purpose of this systematic review is to provide clinicians with an overview of available literature regarding the use of ritonavir-boosted atazanavir (ATV/r) in women. METHODS: The online databases PubMed and EMBASE, HIV-related conference abstracts and reference lists of relevant articles were searched according to predefined terms and limited to items published from 1 October 2007 to 1 October 2012. Updates to conference presentations were checked for substantive journal publication up to 28 November 2013. RESULTS: Of the 294 initial citations retrieved, manual selection identified 19 relevant publications describing gender-based analyses of ATV/r. Publications describing gender-based differences in efficacy, safety, tolerability, pharmacokinetics, drug-drug interactions and adherence are critically evaluated. CONCLUSIONS: As part of a combination antiretroviral therapy regimen, ATV/r appears to be a safe, effective and durable option for treatment-naive and early treatment-experienced patients with HIV-1 infection, including non-pregnant and pregnant women.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".