Bibliographic record
Abstract
INTRODUCTION: HIV, a major cause of morbidity and mortality worldwide has been transformed by antiretroviral (ARV) therapy into a manageable condition. Drug resistance, tolerability and drug interactions remain major concerns when choosing ARV therapy. Raltegravir , the first integrase inhibitor in the armamentarium against HIV, has been shown to be efficacious in both treatment-naïve and treatment-experienced patients when used in combination as part of nucleoside-reverse transcriptase inhibitor-containing regimens. Its key advantages include safety, tolerability and fewer drug interactions but it has some important limitations such as a lower barrier to resistance and a twice-daily dosing schedule. Its role in nucleoside-sparing regimens is under investigation. AREAS COVERED: PubMed was searched for publications in English from 2004 to September 2013 using the terms 'raltegravir', 'integrase inhibitor' and 'MK-0518'. Relevant publications were reviewed and reference lists were examined for further publications. Conference abstracts from the Conference on Retroviruses and Opportunistic Infections, Interscience Conference on Antimicrobial Agents and Chemotherapy, and International AIDS Society Conference on HIV Pathogenesis, Treatment, and Prevention for 2013 were also reviewed. EXPERT OPINION: Raltegravir is an important agent for both naïve and experienced HIV patients. Its key features include its tolerability, efficacy and lack of significant drug interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".