Exercise Training in Metabolic and Bariatric Surgery
Bibliographic record
Abstract
Understanding how to incorporate exercise into metabolic and bariatric surgery programs to optimize treatment outcomes is of great interest, as evidenced by 11 reviews published on this topic in 2022 alone.This overview of reviews was conducted to create a single cohesive resource to aid clinicians and researchers by exploring the effects of pre-and postoperative exercise training on health outcomes.A literature search of seven electronic databases was performed (updated 09/2023) and 24 reviews met preset PICOS eligibility criteria and were included: 4 exploring preoperative exercise training, 13 postoperative, and 7 both.Comparing reviews, outcome results were organized as concordant, discordant, or inconclusive, and then categorized into "what we currently know", "what we think we know" and "what we still don't know".We do not currently know the effects of pre-or postoperative exercise training on any outcomes, but we think we know that preoperative exercise training has a positive effect on BMI and 6-minute walking test distance, and postoperative exercise training has a positive effect on body weight and BMI, waist circumference, bone mineral density, 6-minute walking test distance, muscle strength, and systolic blood pressure.Despite the abundance of research, much still needs to be done in terms of enhancing methodological rigor and reporting to achieve greater confidence in our conclusions; recommendations for next research steps are made
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".