Methadone Dosing Strategies in HIV-Infected Injection Drug Users Enrolled in a Directly Observed Therapy Program
Bibliographic record
Abstract
OBJECTIVES: We have measured methadone dose adjustments and treatment responses after nevirapine (NVP)-, efavirenz (EFV)-, ritonavir-boosted lopinavir (LPV/r), or atazanavir (ATV; with or without ritonavir)-based highly active antiretroviral therapy (HAART) was initiated in injection drug users (IDUs). METHODS: We identified 120 IDUs receiving HAART and methadone within a directly observed therapy (DOT) program. Follow-up was according to clinical standards, with changes in methadone dose being made as required to achieve clinical stabilization within the first 3 months of HAART. RESULTS: The observed median methadone dose changes from baseline were 20 mg/d (P<0.001) in patients on NVP, with 32 (86%) of 37 patients requiring daily dose increases, and 7.5 mg/d (P=0.004) in patients on EFV, with 11 (61%) of 18 patients requiring daily dose increases. Conversely, median changes were 0 mg/d for patients on LPV/r (P=0.56) or ATV (P=0.95). Virologic suppression (HIV RNA<400 copies/mL) was achieved in 26 (70%) of 37, 12 (67%) of 18, 25 (76%) of 33, and 24 (75%) of 32 patients receiving NVP-, EFV-, LPV/r-, and ATV-based regimens, respectively (P=0.89). CONCLUSIONS: Although methadone-based DOT can be a successful tool for the coadministration of HAART, careful monitoring is required to ensure that methadone withdrawal does not adversely affect the goals of treatment, particularly when nonnucleoside reverse transcriptase inhibitors are used.
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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".