Influence of a clinical lifestyle‐based weight loss program on the metabolic risk profile of metabolically normal and abnormal obese adults
Bibliographic record
Abstract
OBJECTIVE: It is unclear whether all obese individuals should be prescribed weight loss (WL) treatment. The effect of a clinically significant WL of 5% on metabolic factors among metabolically normal and abnormal overweight and obese (MNO and MAO) individuals was examined. DESIGN AND METHODS: The sample included 392 overweight and obese adults from the Wharton Medical Clinic. MAO was defined as having one or more clinically relevant aberrations in glucose, triglycerides, blood pressure (BP), high-density lipoprotein-C, low-density lipoprotein-C, preexisting, or current medication use for metabolic conditions. RESULTS: Of the 392 patients, 21.2% of the sample was MNO at baseline and 41.3% of the sample attained a 5% WL. Regardless of initial metabolic health status, improvements in most risk factors were observed with a 5% WL in comparison with those who did not lose weight. Even MAO patients who did not achieve a 5% WL still significantly improved BP and cholesterol over the treatment period. CONCLUSIONS: A clinically significant WL is beneficial for the cardiometabolic risk profile of both MNO and MAO. However, a 5% WL is not necessarily required to improve the cardiometabolic risk profile of MAO. Thus, lifestyle-based WL provides beneficial metabolic effects for all overweight and obese individuals, particularly those with significant metabolic aberrations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".