Reconsidering First‐Line Antiretroviral Therapy in Resource‐Limited Settings: The Need for Operational Research
Bibliographic record
Abstract
Adlington et al. [1] suggest that we should consider the use of boosted protease inhibitor regimens instead of nonnucleoside reverse-transcriptase inhibitor-based regimens in certain resource-limited settings. We tend to agree with the authors for the reasons they state, with the proviso that this plan is implemented in settings that actively monitor the relative long-term effectiveness of these treatment strategies in the field Public health decisions based solely on efficacy findings from randomized clinical trials and/or experience in settings with fewer constraints on resources may, for some settings and populations, prove to be misguided. This is a clear area in which concomitant operational research is needed. The current worldwide rollout of antiretroviral therapy desperately needs a concomitant ramp-up in research on operational factors, as called for in the Sydney Declaration by the International AIDS Society [2]. This declaration calls on national governments as well as bilateral, multilateral, and private donors to allocate 10% of all HIV-programming resources to research that is directed toward optimizing the interventions in use and the health outcomes of the target population. Individuals supporting such operational research can sign the Sydney Declaration at the International AIDS Society Web site (available at: http://www.iasociety.org/Default.aspx?pageId=63)
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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 | 0.270 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.024 | 0.052 |
| Open science | 0.014 | 0.017 |
| Research integrity | 0.023 | 0.032 |
| Insufficient payload (model declined to judge) | 0.009 | 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".