Metabolically healthy and unhealthy obese – the 2013 <scp>S</scp>tock <scp>C</scp>onference report
Bibliographic record
Abstract
Obesity is closely associated with cardiovascular diseases and type 2 diabetes, but some obese individuals, despite having excessive body fat, exhibit metabolic health that is comparable with that of lean individuals. The 'healthy obese' phenotype was described in the 1980s, but major advancements in its characterization were only made in the past five years. During this time, several new mechanisms that may be involved in health preservation in obesity were proposed through the use of transgenic animal models, use of sophisticated imaging techniques and in vivo measurements of insulin sensitivity. However, the main obstacle in advancing our understanding of the metabolically healthy obese phenotype and its related long-term health risks is the lack of a standardized definition. Here, we summarize the proceedings of the 13th Stock Conference of the International Association of the Study of Obesity. We describe the current research and highlight the unanswered questions and gaps in the field. Better understanding of metabolic health in obesity will assist in therapeutic decision-making and help identify therapeutic targets to improve metabolic health in obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".