Very low energy diets prior to bariatric surgery may reduce postoperative morbidity: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Purpose To optimize patients prior to bariatric surgery, very low energy diets (VLEDs) are often employed for 2–4 weeks preoperatively. They are known to result in preoperative weight loss, decrease liver volume, and decrease surgeon-perceived operative difficulty. Their impact on postoperative morbidity has been less extensively studied. We performed a focused systematic review and meta-analysis with the aim of comparing preoperative VLEDs prior to bariatric surgery with controls in terms of overall postoperative morbidity. Methods MEDLINE, Embase, and CENTRAL were searched from database inception to February 2023. Articles were eligible for inclusion if they were randomized controlled trials (RCTs) comparing postoperative morbidity in adult patients (i.e., over the age of 18) receiving a VLED with liquid formulation to those receiving a non-VLED control prior to elective bariatric surgery. Outcomes included overall 30-day postoperative morbidity and preoperative weight loss. An inverse variance meta-analysis was performed with GRADE assessment of the quality of evidence. Results After reviewing 2,525 citations, four RCTs with 294 patients receiving preoperative VLEDs with liquid formulation and 294 patients receiving a non-VLED control met inclusion. Patients receiving VLED experienced significantly more preoperative weight loss than patients receiving control (mean difference (MD) 3.38 kg, 95% confidence interval (CI) 1.06–5.70, p = 0.004, I2 = 95%). According to low certainty evidence, there was a non-significant reduction in 30-day postoperative morbidity in patients receiving VLED prior to bariatric surgery (risk ratio (RR) 0.67, 95%CI 0.39–1.17, p = 0.16, I2 = 0%). Conclusion The impact of preoperative VLEDs on postoperative outcomes following bariatric surgery remains unclear. It is possible that VLEDs may contribute to decreased postoperative morbidity, but further larger prospective trials are required to investigate the signal identified in this study.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".