Macro- and Microvascular Disease in an Insulin-Resistant Pre-Diabetic Animal Model
Bibliographic record
Abstract
The metabolic syndrome is a particularly insidious disease state due to the asymptomatic character of its early stages. A typical clinical history involves a long period of increasing abdominal obesity without obvious underlying disease, followed by an apparently sudden development of frank type 2 diabetes. As the clinical diabetes becomes recognized and treated, medical follow-up reveals established cardiovascular disease (CVD). The associated significant damage to the vascular system develops steadily, beginning during the pre-diabetic period, a process that is only now becoming widely appreciated. Thus, a crucial feature of the metabolic syndrome is widespread endothelial and vascular dysfunction that develops during the pre-diabetic and early diabetic phases of type 2 diabetes. Similarly, the concomitant insulin resistance and hyperinsulinaemia appear to be major determinants of early stage vasculopathy, atherosclerosis, ischemic cardiovascular disease, and glomerular sclerosis, leading to end-stage renal complications ( 1 , 2 ). The metabolic syndrome thus encompasses the primary elements that contribute to the widespread burden of ischemic disease of the heart and brain and renal failure in prosperous societies worldwide. Recently, we have begun to appreciate that the metabolic syndrome and its pathophysiological complications are modulated by complex interactions between the environment (in the broadest sense) and the genome ( 3 ). However, prevention and even amelioration of this disease burden will require greater understanding of the underlying mechanisms. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".