The Obesity Epidemic
Bibliographic record
Abstract
The prevalence of obesity is increasing at an alarming rate and with it the occurrence of a number of comorbidities, including cardiovascular disease, type 2 diabetes, stroke, and certain types of cancer. Approximately one third of adults in the US population are currently classified as obese. Globally, >400 × 106 adults are classified as obese, a number that is expected to double by 2015. Consequently, obesity and its associated health problems are placing a large burden on an already overwhelmed healthcare system. The worldwide costs attributable to this disease are estimated to be between 0.7% and 2.8% of total healthcare expenditures. Furthermore, medical costs for obese individuals are substantially higher than for nonobese people. Obesity is a multifactorial disease caused by a chronic energy surplus in which energy intake exceeds energy expenditure, leading to the accumulation of excess adipose tissue. Regulation of energy homeostasis is a complex process, and that fact imparts a considerable challenge in trying to elucidate the pathogenesis of obesity. Although poor lifestyle choices, including inappropriate diets and lack of physical activity, undoubtedly play a large role, genetic susceptibility also puts an individual at increased risk.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.015 |
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".