Garenoxacin pharmacokinetics in patients undergoing maintenance hemodialysis
Bibliographic record
Abstract
Abstract Introduction: Studies on the pharmacokinetics of the antibiotic garenoxacin (GRNX) in patients with renal insufficiency are lacking. In this study, we attempted to ascertain the appropriate dose of GRNX in patients undergoing maintenance hemodialysis (MH) based on pharmacokinetic parameters and clinical outcomes. Methods:Six male patients with infections who were undergoing MH received 200 mg GRNX once daily. Blood samples were taken before and at 1, 2, 4, 6, 12, and 24 hours after GRNX administration. Plasma GRNX concentrations were measured using high‐performance liquid chromatography. Findings: The mean maximum plasma concentration (Cmax) was 3.00 ± 1.12 µg/mL, time to maximum plasma concentration (Tmax) was 3.0 ± 2.0 hours, and area under the curve for 24 hours (AUC0–24) was 40.7 ± 16.7 µg·h/mL. The half‐life (T1/2) of GRNX could not be calculated because plasma concentrations remained high 24 hours after administration. Cmax was strongly associated with the GRNX dose per kilogram body weight (r = 0.85, P = 0.03). Clinically, fever resolved within 3 days of GRNX administration and C‐reactive protein levels returned to normal 14 days after administration. One patient experienced temporary increases in serum transaminase levels. Discussion: MH patients receiving 200 mg GRNX once daily for infection showed a reduced Cmax but similar AUC0–24 compared with healthy individuals. While this study evaluated the effect of GRNX treatment, further research is needed to assess the accumulation of GRNX and the impact of continuous administration on its pharmacokinetics, as well as to prevent the development of resistant mutants.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".