Obesity is a sign – over‐eating is a symptom: an aetiological framework for the assessment and management of obesity
Bibliographic record
Abstract
Obesity is characterized by the accumulation of excess body fat and can be conceptualized as the physical manifestation of chronic energy excess. Using the analogy of oedema, the consequence of positive fluid balance or fluid retention, obesity can be seen as the consequence of positive energy balance or calorie 'retention'. Just as the assessment of oedema requires a comprehensive assessment of factors related to fluid balance, the assessment of obesity requires a systematic assessment of factors potentially affecting energy intake, metabolism and expenditure. Rather than just identifying and describing a behaviour ('this patient eats too much'), clinicians should seek to identify the determinants of this behaviour ('why, does this patient eat too much?'). This paper provides an aetiological framework for the systematic assessment of the socio-cultural, biomedical, psychological and iatrogenic factors that influence energy input, metabolism and expenditure. The paper discusses factors that affect metabolism (age, sex, genetics, neuroendocrine factors, sarcopenia, metabolically active fat, medications, prior weight loss), energy intake (socio-cultural factors, mindless eating, physical hunger, emotional eating, mental health, medications) and activity (socio-cultural factors, physical and emotional barriers, medications). It is expected that the clinical application of this framework can help clinicians systematically assess, identify and thereby address the aetiological determinants of positive energy balance resulting in more effective obesity prevention and management.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".