The effectiveness and safety of pharmaceuticals to manage excess weight post-bariatric surgery: a systematic literature review
Bibliographic record
Abstract
Objective: To systematically review the literature on weight management pharmaceutical use in patients who have had bariatric surgery.Methods: Google Scholar, Pubmed, Cochrane, Embase, Web of Science, and Clinical Trials were searched from inception to December 31st, 2018 inclusive.Results: Thirteen studies met inclusion and reported decreases in weight with the use of weight management medications in post-bariatric surgical patients. Five studies examined weight loss outcomes by the type of bariatric surgery procedure, and four of these studies observed less weight loss in patients who had undergone gastric sleeve compared to those who had roux-en-y bypass (n = 3 papers) and adjustable gastric banding (n = 1 paper) with medication use. Four studies compared the effectiveness of medications for weight management and observed slightly greater weight loss with the use of topiramate and phentermine as a monotherapy compared to other weight loss medications. Using a sub-sample of participants, authors observed less weight loss on metformin but not phentermine or topiramate for younger adults. Another post-hoc analysis in the same sample observed greater weight loss for older adults with liraglutide 1.8 mg. Side effects were reported in seven studies and were overall consistent with those previously reported in non-surgical populations.Conclusion: Results of this systematic review suggest pharmacotherapy may be an effective tool as an adjunct to diet and physical activity to support weight loss in post-bariatric surgery patients. However, due to most studies lacking a control or placebo group, more rigorous research is required to determine the efficacy of this intervention.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".