The urgent need for more potent antiretroviral therapy in low-income countries to achieve UNAIDS 90-90-90 and complete eradication of AIDS by 2030
Bibliographic record
Abstract
BACKGROUND: Over 90% of Human Immunodeficiency Virus (HIV) infected individuals will be on treatment by 2020 under UNAIDS 90-90-90 global targets. Under World Health Organisation (WHO) "Treat All" approach, this number will be approximately 36.4 million people with over 98% in low-income countries (LICs). MAIN BODY: Pretreatment drug resistance (PDR) largely driven by frequently use of non-nucleoside reverse transcriptase inhibitors (NNRTIs), efavirenz and nevirapine, has been increasing with roll-out of combined antiretroviral therapy (cART) with 29% annual increase in some LICs countries. PDR has exceeded 10% in most LICs which warrants change of first line regimen to more robust classes under WHO recommendations. If no change in regimens is enforced in LICs, it's estimated that over 16% of total deaths, 9% of new infections, and 8% of total cART costs will be contributed by HIV drug resistance by 2030. Less than optimal adherence, and adverse side effects associated with currently available drug regimens, all pose a great threat to achievement of 90% viral suppression and elimination of AIDS as a public health threat by 2030. This calls for urgent introduction of policies that advocate for voluntary and compulsory drug licensing of new more potent drugs which should also emphasize universal access of these drugs to all individuals worldwide. CONCLUSIONS: The achievement of United Nations Programme on HIV and AIDS 2020 and 2030 targets in LICs depends on access to active cART with higher genetic barrier to drug resistance, better safety, and tolerability profiles. It's also imperative to strengthen quality service delivery in terms of retention of patients to treatment, support for adherence to cART, patient follow up and adequate drug stocks to help achieve a free AIDS generation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".