Nevirapine Concentrations in Saliva Measured by Thin Layer Chromatography and Self-Reported Adherence in Patients on Antiretroviral Therapy at Kilimanjaro Christian Medical Centre, Tanzania
Bibliographic record
Abstract
BACKGROUND: Thin layer chromatography (TLC) can be used to perform therapeutic drug monitoring in resource-limited settings, where more expensive analytical methods, such as high-performance liquid chromatography or liquid chromatography-mass spectrometry, are not feasible. OBJECTIVES: The aim of this cross-sectional study was to compare saliva concentrations of nevirapine (NVP) with self-reported adherence in patients on NVP-containing antiretroviral treatment at Kilimanjaro Christian Medical Centre, Moshi, Tanzania. METHODS: HIV-infected patients using a combination of zidovudine + lamivudine + NVP, or stavudine + lamivudine + NVP, for more than 4 weeks were included. Saliva samples were collected using dental cotton rolls impregnated with citric acid (20 mg). Saliva NVP concentrations were analyzed using TLC. Adherence to ARV medication was assessed by self-reporting using the Morisky scale. RESULTS: Of the 91 study participants, 79 (86.8%) had therapeutic saliva NVP concentrations (ie, >1.75 mg/L) and 12 (13.2%) had subtherapeutic concentrations. Self-reported adherence among the study participants was high in 62 participants (68.1%), moderate in 24 (26.4%), and low in 5 (5.5%). Fifty-seven (91.9%) of the study participants with high self-reported adherence had therapeutic saliva NVP concentrations. Of the 5 participants with low self-reported adherence, 3 had therapeutic NVP concentrations. CONCLUSIONS: A high proportion of patients had therapeutic NVP saliva concentrations as measured by TLC, which showed a good agreement with self-reported adherence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".