Are Abdominal Obese Metabolically Healthy Phenotype a Benign Condition? Protocol for a Systematic Review
Bibliographic record
Abstract
Background: The prevalence of obesity is increasing worldwide. Obesity is associated with severe health effects. Abdominal obesity has a strong association with metabolic dysfunction. A subgroup of people with central obesity has been identified without typical metabolic disorders associated with obesity that has been known metabolically healthy abdominal obese (MHAO). The purpose of this review is to evaluate the MHAO phenotype in the context of type 2 DM incidence, risk of cardiovascular diseases, and all-cause of mortality. Methods: This is a protocol of systematic review. We will search PubMed/MEDLINE, EMBASE, Web of Science, Cochrane Library, and ProQuest. Additional studies will be identified through manual searching of reference lists. Quantitative studies evaluating abdominal obesity phenotype outcomes in adults will be included. Primary results will be assaying abdominal obesity phenotype results, including DM2 incidence, cardiovascular disease risk, and all-cause mortality. Two reviewers will independently screen full-text articles and abstract data. Statistical Analysis Used: Potential conflicts will be resolved through discussion. Results: The study methodological quality (or bias) will be appraised using appropriate tools. If feasible, we will conduct a random-effects meta-analysis. The researchers will also assess the quality of the articles independently based on Newcastle-Ottawa scale. Conclusions: The results of this review will provide a useful reference for the effect of abdominal obesity on metabolic dysfunction and cardiovascular or all-cause mortality.
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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 | 0.056 | 0.073 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.007 |
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".