Challenges in Initiating Antiretroviral Therapy in 2010
Bibliographic record
Abstract
Many clinical trials have shown that initiating antiretroviral therapy (ART) at higher rather than lower CD4 T cell-positive counts results in survival benefit. Early treatment can help prevent end-organ damage associated with HIV replication and can decrease infectivity. The mainstay of treatment is either a non-nucleoside reverse transcriptase inhibitor or a ritonavir-boosted protease inhibitor in combination with two nucleoside reverse transcriptase inhibitors. While effective at combating HIV, ART can produce adverse alterations of lipid parameters, with some studies suggesting a relationship between some anti-retroviral agents and cardiovascular disease. As the HIV-positive population ages, issues such as hypertension and diabetes must be taken into account when initiating ART. Adhering to ART can be difficult; however, nonoptimal adherence to ART can result in the development of resistance; thus, drug characteristics and the patient's preparedness to begin therapy must be considered. Reducing the pill burden through the use of fixed-dose antiretroviral drug combinations can facilitate adherence.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".