Bibliographic record
Abstract
Obesity is a worldwide epidemic, with the number of overweight and obese individuals climbing from just over 500 million in 2008 to 1.9 billion in 2014. Type 2 diabetes (T2D), cardiovascular disease and non-alcoholic fatty liver disease have long been associated with the obese state, whereas cancer is quickly emerging as another pathological consequence of this disease. Globally, at least 2.8 million people die each year from being overweight or obese. It is estimated that by 2020 being overweight or obese will surpass the health burden of tobacco consumption. Increase in the body mass index (BMI) in overweight (BMI>25 kg/m(2)) and obese (BMI>30 kg/m(2)) individuals is a result of adipose tissue (AT) expansion, which can lead to fat comprising >50% of the body weight in the morbidly obese. Extensive research over the last several years has painted a very complex picture of AT biology. One clear link between AT expansion and etiology of diseases like T2D and cancer is the development of insulin resistance (IR) and hyperinsulinemia. This review focuses on defining the link between obesity, IR and cancer.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".