Pharmacokinetic changes post–bariatric surgery: A scoping review
Bibliographic record
Abstract
Bariatric surgeries induce structural changes that can alter the absorption of drugs in patients already at risk of polypharmacy. This scoping review aimed to explore pharmacokinetic changes of orally administered drugs in patients post-bariatric surgery, and assess the quality and level of bias. Electronic databases were searched for articles relating to bariatric surgery and pharmacokinetics published between 1998 and 2019. Pre-post studies reporting on pharmacokinetic parameters were included, and the Newcastle-Ottawa Scale was used to assess risk-of-bias. A total of 21 studies were included in this review, and changes in absorption were reported in all included studies across 29 drugs. In 11 studies, this change was reported as statistically significant (p<.05), while six reported a nonsignificant change. More drugs exhibited a shorter Tmax and higher Cmax after surgery than otherwise, however changes in AUC were variable. Four studies were assessed as having fair quality while the remainder of the included studies were of good quality and low risk-of-bias. Bariatric surgery alters the absorption of drugs and several mechanisms are implicated to be responsible. Short and long-term monitoring is recommended in patients post-surgery for clinical changes in response to medications. Future research with a higher number of participants and greater control of variables, such as concurrent medications, malabsorptive disorders, and body composition 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".