General principles of pharmacotherapy for the patient with HIV infection
Bibliographic record
Abstract
Introduction Over the past several years, highly active antiretroviral therapy (HAART) has become the pharmacological mainstay in the ongoing management of HIV infection and AIDS. This treatment regimen comprises a combination of antiretroviral medications which fall into four major classes including nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), and entry inhibitors (Table 3.1). Each medication within its respective class acts to inhibit the replication process of HIV at a distinct point in its viral life cycle. When used in combination therapy, these medications form a highly effective and powerful tool in the treatment of HIV infection and AIDS. However, as effective and beneficial as these combination antiretroviral treatments are, the ongoing management of HIV infection may be complicated by potential side effects and drug–drug interactions. In addition to being concerned about the impact of antiretrovirals on psychotropic medications, the clinician has to ensure that the psychopharmacological agents do not compromise HIV treatment or lead to the development of resistant strains of the virus. This chapter: describes the various potential drug–drug interactions and neuropsychiatric side effects that may occur when psychotropic medications, narcotics, recreationally used/abused drugs, and alternative agents are utilized concomitantly with antiretroviral treatment offers practical recommendations on choosing psychotropic medications for a variety of psychiatric illnesses while respecting issues of safety and not compromising HIV care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.030 |
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".