Structured Treatment Interruptions: A Risky Business
Bibliographic record
Abstract
HAART has dramatically altered the natural history of HIV disease [1–5]. However, currently available treatments are suppressive and cannot eradicate HIV infection [6–8]. As a result, antiretroviral therapy in 2004 represents a life-long proposition requiring exceptionally good—if not perfect—adherence [9]. Unfortunately, many issues, including the complexity of the regimens, drug toxicities, drug interactions, lifestyle issues, and comorbidities, may interfere with achievement of this goal [10–14]. In recent years, a variety of therapeutic strategies have been developed to alleviate some of these problems: several safer and better-tolerated agents have been approved, simpler regimens with once-daily dosing are now possible, and fixed-dose combinations of antiretroviral agents are also available. In addition, deferring initiation of antiretroviral therapy has been widely recommended [15–18]. As an alternative approach with the potential to reduce drug exposure, promote adherence to therapy, and decrease treatment-related fatigue, several treatment interruption strategies are currently under evaluation [19]. Treatment interruptions have been explored in various distinct clinical situations with different aims. For patients who initiated therapy during acute seroconversion, treatment interruption has been explored as a means to enhance HIV-specific immune response and, consequently, allow better control of viral replication in the absence of continued therapy [20, 21]. This remains an attractive yet elusive hypothesis. Until results of ongoing, prospective, controlled clinical trials are available, this intervention should be regarded as experimental in nature and cannot be recommended.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.098 | 0.066 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".