Medication and supplement pharmacokinetic changes following bariatric surgery: A systematic review and meta‐analysis
Bibliographic record
Abstract
Summary Objectives To evaluate the impact of bariatric surgery on the pharmacokinetic (PK) parameters of orally administered medications and supplements. Methods Systematic searches of bibliographic databases were conducted to identify studies. Pooled effect estimates from different surgical procedures were calculated using a random‐effects model. Results Quantitative data were synthesized from 58 studies including a total of 1985 participants. Whilst 40 medications and 6 supplements were evaluated across these studies, heterogeneity and missing information reduced the scope of the meta‐analysis to the following medications and supplements: atorvastatin, paracetamol, omeprazole, midazolam, vitamin D, calcium, zinc, and iron supplements. There were no significant differences in PK parameters post‐surgery for the drugs atorvastatin and omeprazole, and supplements calcium, ferritin, and zinc supplements. Paracetamol showed reduced clearance (mean difference [MD] = −15.56 L/hr, p = 0.0002, I 2 = 67%), increased maximal concentration (MD = 6.90 μg/ml, p = 0.006, I 2 = 92%) and increased terminal elimination half‐life (MD = 0.49 hr, p < 0.0001, I 2 = 3%) post‐surgery. The remaining 36 medications and 2 supplements were included in a systematic review. Overall, 18 of the 53 drugs and supplements showed post‐operative changes in PK parameters. Conclusion This study demonstrates heterogeneity in practice and could not reach conclusive findings for most PK parameters. Prospective studies are needed to inform best practice and enhance patient healthcare and safety following bariatric surgery.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".