Planning <scp>HIV</scp> therapy to prevent future comorbidities: patient years for tenofovir alafenamide
Bibliographic record
Abstract
Since the introduction of suppressive antiretroviral therapy (ART), HIV has become a chronic disease, with infected people in high-income countries approaching similar life expectancy to the general population. As this population ages, an increasing number of people with HIV are living with age-, treatment-, and disease-related comorbidities. Lifestyle factors such as smoking, alcohol abuse, and substance misuse have a role in age-related comorbidity. Some degree of immune dysfunction is suggested by the presence of markers of immune activation/inflammation despite effective suppression of HIV replication. Cumulative exposure to some antiretroviral drugs contributes to HIV-associated comorbidities, with risk increasing with age. Specifically, tenofovir disoproxil fumarate (TDF), ritonavir-boosted atazanavir, and ritonavir-boosted lopinavir are associated with renal impairment, and TDF is known to cause loss of bone mineral density. Tenofovir alafenamide (TAF) was developed to improve on the safety profile of TDF, while maintaining its efficacy. TAF has better stability in plasma, and higher intracellular accumulation of tenofovir diphosphate in target cells, which has resulted in improved antiviral activity at lower doses with improved renal and bone safety. TAF has been studied extensively in randomized clinical trials and real-world studies. TAF-based regimens are recommended over TDF-containing regimens for the improved safety profile.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".