Clinical outcomes and immune benefits of anti-epileptic drug therapy in HIV/AIDS
Bibliographic record
Abstract
BACKGROUND: Anti-epileptic drugs (AEDs) are frequently prescribed to persons with HIV/AIDS receiving combination antiretroviral therapy (cART) although the extent of AED use and their interactions with cART are uncertain. Herein, AED usage, associated toxicities and immune consequences were investigated. METHODS: HIV replication was analysed in proliferating human T cells during AED exposure. Patients receiving AEDs in a geographically-based HIV care program were assessed using clinical and laboratory variables in addition to assessing AED indication, type, and cumulative exposures. RESULTS: Valproate suppressed proliferation in vitro of both HIV-infected and uninfected T cells (p <0.05) but AED exposures did not affect HIV production in vitro. Among 1345 HIV/AIDS persons in active care between 2001 and 2007, 169 individuals were exposed to AEDs for the following indications: peripheral neuropathy/neuropathic pain (60%), seizure/epilepsy (24%), mood disorder (13%) and movement disorder (2%). The most frequently prescribed AEDs were calcium channel blockers (gabapentin/pregabalin), followed by sodium channel blockers (phenytoin, carbamazepine, lamotrigine) and valproate. In a nested cohort of 55 AED-treated patients receiving cART and aviremic, chronic exposure to sodium and calcium channel blocking AEDs was associated with increased CD4+ T cell levels (p <0.05) with no change in CD8+ T cell levels over 12 months from the beginning of AED therapy. CONCLUSIONS: AEDs were prescribed for multiple indications without major adverse effects in this population but immune status in patients receiving sodium or calcium channel blocking drugs was improved.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".