Type 2 diabetes and cardiovascular disease: getting to the fat of the matterThis paper is one of a selection of papers published in this Special Issue, entitled Young Investigators' Forum.
Bibliographic record
Abstract
The increasing national prevalence of obesity is a major public health concern and a substantial burden on the health care resources of Canada. In addition to the direct health impact of obesity, this condition is a well-established risk factor for the development of various prevalent comorbidities including type 2 diabetes, hypertension, and cardiovascular disease. Historically, adipose tissue has been regarded primarily as an organ for energy storage. However, the discovery of leptin in the mid 1990's revolutionized our understanding of this tissue and has focused attention on the endocrine function of adipose tissue as a source of secreted bioactive peptides. These compounds, collectively termed adipokines, regulate a number of biological functions including appetite and energy balance, insulin sensitivity, lipid metabolism, blood pressure, and inflammation. The physiological importance of adipokines has led to the hypothesis that changes in the synthesis and secretion of these compounds in the obese are a causative factor contributing to the development of obesity and obesity-related diseases in these individuals. Following from this it has been proposed that pharmacologic manipulation of adipokine levels may provide novel effective therapeutic strategies to treat and prevent obesity, type 2 diabetes, and cardiovascular disease.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".