Bibliographic record
Abstract
Introduction Protease inhibitors (PIs) like Amprenavir (APV), Atazanavir (ATV), Darunavir (DRV), Indinavir (IDV), Lopinavir (LPV), Nelfinavir (NLF), Saquinavir (SQV) and Tipranavir (TPV) are widely used in highly active antiretroviral therapy (HAART) of HIV-infected patients.To improve individualized therapy it is crucial to know how age affects drug levels. Methods and MaterialsIn a retrospective study we analyzed 4741 consecutive steady state plasma concentrations of eight PIs during routinely performed Therapeutic Drug Monitoring of HIV-patients.Determination of trough levels was conducted by HPLC.For statistical analysis patients were assigned to at most four groups of different age.Median (med) and Inter-Quartile-Range (IQR) are reported.Besides descriptive statistical analysis nonparametric Mann--Whitney-U test was used to detect differences between two groups.Results Significant higher levels of ATV were found in the group of patients younger than 30 years (n = 28, med (IQR) = 1902 (2113) ng/ml) than in the group between 30 and 40 years (n = 115, med (IQR) = 1004 (1095) ng/ml; P = 0.007).Drug levels remained stable in the two classes of older patients [40--50 years: n = 148, med (IQR) = 1184 (1457) ng/ml; >50 years: n = 89, med (IQR) = 1093 (1420) ng/ml)].An obvious difference in the drug level of NLF could not be proved to be significant [<40 years: n = 17, med (IQR) = 1839 (2201) ng/ml; >40 years: n = 26, med (IQR) = 997 (1159) ng/ml); P = 0.08].Plasma concentrations of remaining PIs showed stable levels or only few fluctuations.Conclusion Pharmacokinetic parameters of most investigated PIs are independent from age.However ATV and NLF showed higher levels in younger patients than in older.For ATV this difference was even highly significant.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.681 | 0.404 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".