Bibliographic record
Abstract
Once considered a disease of the affluent, obesity now prevails in the poorer developing nations as well.1 Although it is widely acknowledged that obesity, together with its allied metabolic disorders, is closely associated with the pathogenesis of cardiovascular diseases (CVDs),1,2 the precise molecular links between obesity and CVD remain speculative. It is evident that adipose tissue, traditionally viewed as a passive energy reservoir, plays a role in homeostasis and metabolism. Adipose tissue has autocrine, paracrine, and endocrine functions and synthesizes and releases a wide array of cytokine-like products collectively termed adipokines. Adipokines include pro- and anti-inflammatory molecules, complement factors, growth factors, and signalling proteins2 that modulate inflammatory, metabolic, and cardiovascular events.2 Adiponectin is an adipocyte-specific protein that circulates in concentrations greater than any other hormone in the body. In contrast to many of the other adipokines, adiponectin appears to offer cardiovascular and metabolic protection via insulin sensitizing, anti-inflammatory, lipid metabolism, anti-atherogenesis, and anti-angiogenic effects, which, in part, are mediated through adiponectin receptors.3 Adiponectin accumulates in the sub-endothelium of injured human arteries where it inhibits monocyte adhesion to endothelial cells and ultimately inhibits the migration and proliferation of vascular smooth muscle that contribute to the atherosclerotic process.4 Adiponectin levels are lower in females when compared with males, obese subjects vs. lean subjects, and type 2 diabetics vs. non-diabetics. There is a strong negative correlation between plasma adiponectin levels and visceral fat, as well as body mass index (BMI) in both humans and animals. Low levels of adiponectin are positively associated with an increased incidence and risk of obesity, diabetes mellitus, insulin resistance, low HDL, high triglycerides, and ultimately the development of vascular diseases.5 Cavusoglu et al. 6 reported on a 2-year study that included 325 high-risk males with cardiac-related chest pain and underwent … *Corresponding author. Tel: +1 416 782 0092; fax: +1 416 782 0096. E-mail address : subodh.verma{at}sympatico.ca
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".