Oseltamivir pharmacokinetics in morbid obesity (OPTIMO trial)
Bibliographic record
Abstract
BACKGROUND: Detailed pharmacokinetics to guide oseltamivir (Tamiflu®) dosing in morbidly obese patients is lacking. METHODS: The OPTIMO trial was a single-centre, non-randomized, open-label pharmacokinetic study of single-dose and steady-state oral oseltamivir phosphate and its carboxylate metabolite in healthy, morbidly obese [body mass index (BMI) > 40)] and healthy, non-obese (BMI < 30) subjects. RESULTS: In the morbidly obese versus control subjects, respectively, the single-dose median oseltamivir oral clearance (CL/F) [840 (range 720-1640) L/h versus 580 (470-1800) L/h] was higher, the area under the curve from time zero to infinity (AUC(0-∞)) [89 (46-104) ng·h/mL versus 132 (42-160) ng·h/mL] was lower and the volume of distribution (V/F) [2320 (900-8210) L versus 1670 (700-7290) L] was unchanged. In the morbidly obese versus control subjects, respectively, the single-dose median oseltamivir carboxylate CL/F [22 (17-40) L/h versus 23 (12-33) L/h], AUC(0-∞) [3100 (1700-4100) ng·h/mL versus 3000 (2100-5900) ng·h/mL] and V/F [200 (130-370) L versus 260 (150-430) L] were similar. Similar results for oseltamivir and oseltamivir carboxylate CL/F, AUC₀₋₁₂ and V/F values were observed in the multiple-dose study. CONCLUSIONS: With single and multiple dosing, the systemic exposure to oseltamivir is decreased but that of oseltamivir carboxylate is largely unchanged. Based on these pharmacokinetic data, an oseltamivir dose adjustment for body weight would not be needed in morbidly obese individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".