A comprehensive diagnostic approach to detect underlying causes of obesity in adults
Bibliographic record
Abstract
Obesity is a worldwide growing problem. When confronted with obesity, many health care providers focus on direct treatment of the consequences of adiposity. We plead for adequate diagnostics first, followed by an individualized treatment. We provide experience-based and evidence-based practical recommendations (illustrated by clinical examples), to detect potential underlying diseases and contributing factors. Adult patients consulting a doctor for weight gain or obesity should first be clinically assessed for underlying diseases, such as monogenetic or syndromic obesity, hypothyroidism, (cyclic) Cushing syndrome, polycystic ovarian syndrome (PCOS), hypogonadism, growth hormone deficiency, and hypothalamic obesity. The most important alarm symptoms for genetic obesity are early onset obesity, dysmorphic features/congenital malformations with or without intellectual deficit, behavioral problems, hyperphagia, and/or striking family history. Importantly, also common contributing factors to weight gain should be investigated, including medication (mainly psychiatric drugs, (local) corticosteroids, insulin, and specific β-adrenergic receptor blockers), sleeping habits and quality, crash diets and yoyo-effect, smoking cessation, and alcoholism. Other associated conditions include mental factors such as chronic stress or binge-eating disorder and depression.Identifying and optimizing the underlying diseases, contributing factors, and other associated conditions may not only result in more effective and personalized treatment but could also reduce the social stigma for patients with obesity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".