EFFICACY OF ENDURANCE VERSUS STRENGTH EXERCISE TO INCREASE THE FUNCTIONAL CAPACITY OF OBESE PEOPLE AFTER BARIATRIC SURGERY: A SYSTEMATIC NARRATIVE REVIEW
Bibliographic record
Abstract
Introduction: Obesity has emerged as a global health concern, forcing researchers, policymakers, and healthcare professionals to join forces to combat this multifaceted epidemic. Increasing your level of physical activity is part of your treatment. However, it is not clear which exercise is most suitable. Objective: Synthesize evidence on which type of exercise is most effective on the functional capacity of obese people after bariatric surgery. Methods: Systematic review of randomized clinical trials published in English, Spanish and Portuguese. We searched from April 23 to June 10, 2023 in the MedLine databases via PubMed, Latin American and Caribbean Literature in Health Sciences (LILACS), Excerpta Medica dataBASE (EMBASE), gray literature and the Cochrane Library. We analyzed risk of bias with the Revised Cochrane tool (Rob 2) and certainty of evidence and strength of recommendation with the Recommendation Rating Assessment, Development and Evaluation system. Results: Of the 10,436 publications identified, we included 6 studies, with a population of 175 obese people, mostly women, aged between 18 and 65 years undergoing gastric bypass. The studies were published from 2011 to 2018 in Brazil, Canada, USA, Iran and the United Kingdom. Our synthesis and analysis demonstrated, with a low level of confidence, that resistance exercises, isolated or combined with strength exercises, were more effective in the functional capacity of obese people after bariatric surgery. Conclusion: With a weak recommendation and low level of evidence, we recommend that resistance exercises be used for functional capacity in obese patients after bariatric surgery.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".