Patient adherence and satisfaction and changes in anthropometric parameters with e-health versus in-person monitoring in bariatric surgery candidates: a systematic review and non-inferiority meta-analysis of cohort studies
Bibliographic record
Abstract
Background: Obesity is a risk factor for cardiovascular diseases and associated with reduced life expectancy. Surgery is a treatment approach for weight loss in some cases and patient monitoring is cost-effective and feasible. However, there is no strong evidence on the differences between e-health and in-person monitoring in bariatric surgery candidates. Methods and analyses: This review study will include cohort studies involving individuals with obesity (aged ≥18 years) and e-health or in-person patient monitoring before and after bariatric surgery. We will conduct searches in the following databases: PubMed, EMBASE (Elsevier), Cochrane (CENTRAL), Web of Science, SCOPUS and CINAHL (EBSCO), LILACS-VHL and SciELO. We will also search databases in the gray literature. The primary outcomes will be changes in body mass index (BMI), total body mass (kg) and body fat percentage (BF%) and patient adherence and satisfaction. The risk of bias of individual eligible studies will be assessed using the Newcastle-Ottawa Quality Assessment Scale and the overall quality will be assessed using the GRADE tool. Our analyses will involve comparisons of mean differences (MDs) or standardized mean differences (MSDs) across the groups using random-effects models and 95% confidence intervals. Statistical analyses will be performed with RStudio for Windows (v1.3.959) using R package meta (v3.6.1).
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".