Initial and evolutionary profile of adverse responders to an intensive weight loss intervention: the RESOLVE Study
Bibliographic record
Abstract
BACKGROUND: There is a need to better identify adverse responders to weight-loss interventions. The aim of this study was to: 1) identify potential predictive factors of adverse responders to weight loss; and 2) follow their long-term evolution. METHODS: One-hundred participants (56 females) with overweight (59.5±4.9 years) followed a 3-week intervention combining physical activity and diet followed by one-year monitoring, and were divided into three subgroups: 1) group A (N.=13) - "regainers" (weight regain during follow-up >100% of initial loss); 2) group B (N.=25) - "moderate regainers" (weight regain: 0-100%); and 3) group C (N.=46) - "weight relosers" (weight regain <0%). Body composition, food consumption, inflammatory and metabolic markers were assessed during the intervention and follow-up. RESULTS: Baseline energy intake was lower in group A(1518±361kcal/day) vs. group B (1929±451kcal/day) (P=0.013) and C(1882±572kcal/day) (P=0.024). Group A initially presented a healthier metabolic profile and the total score of compliance (diet + physical activity) was lower in group A during follow-up (group A:38.3% vs. B:49.2%(P=0.007) and C:72.1% (P<0.001). Ghrelin levels tend to decrease and peptide YY (PYY) to increase during follow-up in groups B and C while blunted responses were obtained in A. CONCLUSIONS: Adverse responders might be characterized by a less unhealthy metabolic profile at baseline but also by less favorable changes in their satiety-regulating hormones during the one-year follow-up.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".