The Role of Adherence to Antiretroviral Therapy in the Management of HIV Infection
Bibliographic record
Abstract
Despite multiple studies demonstrating the relation between the success of highly active antiretroviral therapy (HAART) and adherence, inadequate adherence continues to be one of the most frequent reasons for poor treatment outcomes and/or lack of sustained treatment benefits. Interventions targeting patient-related social and psychologic barriers to adherence and issues related to mental health and substance abuse and access to health care may ameliorate their negative impact on adherence. Specific drug-related factors that influence adherence such as pill burden, dosing frequency, food requirements, and acute tolerability and safety concerns, however, are further issues that must be considered to optimize adherence. Fortunately, the availability of once-daily and coformulated agents with simple dosing requirements may help to improve adherence, and thereby make the difference between success and failure of HAART for some patients. A better understanding of adherence and its determinants and how to define specific goals in a given clinical setting are keys for clinicians to become more effective partners with patients in the achievement and maintenance of long-term virologic suppression and, more importantly, long-term health.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".