The downside of weight loss: realistic intervention in body-weight trajectory.
Bibliographic record
Abstract
OBJECTIVE: To explore the reasons why long-term weight loss is seldom achieved and to evaluate the consequences of various weight trajectories, including stability, loss, and gain. QUALITY OF EVIDENCE: Studies evaluating population weight metrics were mainly observational. Level I evidence was available to evaluate the influence of weight interventions on mortality and quality of life. MAIN MESSAGE: Sustained weight loss is achieved by a small percentage of those intending to lose weight. Mortality is lowest in the high-normal and overweight range. The safest body-size trajectory is stable weight with optimization of physical and metabolic fitness. With weight loss there is evidence for lower mortality in those with obesity-related comorbidities. There is also evidence for improved health-related quality of life in obese individuals who lose weight. Weight loss in the healthy obese, however, is associated with increased mortality. CONCLUSION: Weight loss is advisable only for those with obesity-related comorbidities. Healthy obese people wishing to lose weight should be informed that there might be associated risks. A strategy that leads to a stable body mass index with optimized physical and metabolic fitness at any size is the safest weight intervention option.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".