HIV Treatment Adherence, Drug Resistance, Virologic Failure: Evolving Concepts
Bibliographic record
Abstract
Poor adherence to combined antiretroviral therapy (cART) has been shown to be a major determinant of virologic failure, emergence of drug resistant virus, disease progression, hospitalizations, mortality, and health care costs. While high adherence levels can be achieved in both resource-rich and resource-limited settings following initiation of cART, long-term adherence remains a challenge regardless of available resources. Barriers to optimal adherence may originate from individual (biological, socio-cultural, behavioral), pharmacological, and societal factors. Although patients and providers should continuously strive for maximum adherence to cART, there is accumulating evidence that each class of antiretroviral therapy has specific adherence-drug resistance relationship characteristics allowing certain regimens more flexibility than others. There is not a universally accepted measure for cART adherence, since each method has distinct advantages and disadvantages including cost, complexity, accuracy, precision, intrusiveness and bias. Development of a real-time cART adherence monitoring tool will enable the development of novel, pre-emptive adherence-improving strategies. The application of these strategies may ultimately prove to be the most cost-effective method to reduce morbidity and mortality for the individual and decrease the likelihood of HIV transmission and emergence of resistance in the community. Keywords: HIV, antiretroviral therapy adherence, virologic failure, drug resistance, outcomes, resource-rich and resource-limited settings, flexibility, complexity, real-time cART adherence monitoring tool, morbidity and mortality, cART, protease inhibitor, test and treat, HIV prevention strategies
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".