The role of bariatric surgery on kidney transplantation: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Obesity (body mass index [BMI] >35 kg/m2) remains a relative contraindication for kidney transplant, while patients after kidney transplantation (KTX) are predisposed to obesity. The present study aims to investigate the role of bariatric surgery in improving transplant candidacy in patients prior to KTX, as well its safety and efficacy in KTX patients postoperatively. Methods: A systematic search was conducted up to March 2020. Both comparative and non-comparative studies investigating the role of bariatric surgery before or after KTX were considered. Outcomes included change in BMI, rates of mortality and complications, and the rate of patients who underwent KTX following bariatric surgery. Pooled estimates were calculated using the random effects meta-analysis of proportions. Results: Twenty-one studies were eligible for final review; 11 studies investigated the role of bariatric surgery before KTX. The weighted mean BMI was 43.4 (5.7) kg/m2 at baseline and 33.9 (6.3) kg/m2 at 29.1 months followup. After bariatric surgery, 83% (95% confidence interval [CI] 57–99) were successfully listed for KTX and 83% (95% CI 65–97) patients subsequently received successful KTX. Ten studies investigated the role of bariatric surgery after kidney transplant. Weighted mean baseline BMI was 43.8 (2.2) kg/m2 and mean BMI at 19.5 months followup was 34.2 (6.7) kg/m2. Overall, all-cause 30-day mortality was 0.5% for both those who underwent bariatric surgery before or after receiving a KTX. The results of this study are limited by the inclusion of only non-randomized studies, limited followup, and high heterogeneity. Conclusions: Bariatric surgery may be safe and effective in reducing weight to improve KTX candidacy in patients with severe obesity and can also be used safely following KTX.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.008 |
| 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".