Effect of gastric bypass surgery on azithromycin oral bioavailability
Bibliographic record
Abstract
OBJECTIVES: Azithromycin is used widely for community-acquired infections. The timely administration of azithromycin in adequate doses minimizes treatment failure. Gastric bypass, a procedure that circumvents the upper gut, may compromise azithromycin plasma levels. We hypothesized that azithromycin concentrations would be reduced following gastric bypass. METHODS: A single-dose pharmacokinetic study in 14 female post-gastric bypass patients and 14 sex- and body mass index (BMI)-matched controls (mean age 44 years and BMI 36.4 kg/m(2)) was performed. Subjects were administered two 250 mg azithromycin tablets at time 0 and plasma azithromycin levels were sampled at 0.5, 1, 1.5, 2, 3, 5, 7 and 24 h. The AUC of the plasma azithromycin concentrations from time 0 to 24 h (AUC(0-24)) was the primary outcome. RESULTS: Azithromycin concentrations were lower in gastric bypass patients compared with controls throughout the entire duration of sampling. Compared with controls, the AUC(0-24) was reduced in gastric bypass subjects by 32% [1.41 (SD 0.51) versus 2.07 (0.75) mg · h/L; P = 0.008], and dose-normalized AUC(0-24) was reduced by 33% [0.27 (0.12) versus 0.40 (0.13) kg · h/L; P = 0.009]. Peak azithromycin concentrations were 0.260 (0.115) in bypass subjects versus 0.363 (0.200) mg/L in controls (P = 0.08), and were reached at 2.14 (0.99) h in gastric bypass subjects and 2.36 (1.17) h in controls (P = 0.75). CONCLUSIONS: Azithromycin AUC was reduced by one-third in gastric bypass subjects compared with controls. The potential for early treatment failure exists, and dose modification and/or closer clinical monitoring of gastric bypass patients receiving azithromycin should be considered.
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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) | 0.002 | 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".