Extremes of weight gain and weight loss with detailed assessments of energy balance: Illustrative case studies and clinical recommendations
Bibliographic record
Abstract
Extreme weight changes, or changes in weight greater than 10 kg within a 2-year period, can be caused by numerous factors that are much different than typical weight fluctuations. This paper uses two interesting cases of extreme weight change (a female who experienced extreme weight gain and a male who experienced extreme weight loss) from participants in the Energy Balance Study to illustrate the physiological and psychosocial variables associated with the weight change over a 15-month period, including rigorous assessments of energy intake, physical activity (PA) and energy expenditure, and body composition. In addition, we provide a brief review of the literature regarding the relationship between energy balance (EB) and weight change, as well as insight into proper weight management strategies. The case studies presented here are then placed in the context of the literature regarding EB and weight change. This report further supports previous research on the importance of regular doses of PA for weight maintenance, and that even higher volumes of PA are necessary for weight loss. Practitioners should emphasize the importance of PA to their patients and take steps to monitor their patients' involvement in PA.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".