Development of a predictive model for negative microvascular outcomes in the metabolic syndrome
Bibliographic record
Abstract
Evolution of the metabolic syndrome is a multi‐factorial process with a constellation of pathologies developing over time. Consequently, poor vascular outcomes in the metabolic syndrome are also temporally distributed, and an integrated understanding of these outcomes requires information regarding their sequence of development. To address this, we examined development of the metabolic syndrome, systemic disease biomarkers, and negative vascular structural/functional outcomes in obese Zucker rats at 6‐7, 9‐10, 12‐13, 15‐16 and 19‐20 weeks. For these initial procedures and model development, no interventional strategy against either the metabolic syndrome or the poor outcomes was employed. Prior to development of vascular dysfunction, endothelial arachidonic acid metabolism was altered toward TxA 2 production; predicted by insulin resistance. This outcome was followed by an increase in markers of systemic inflammation, resulting in systemic oxidant stress and reduced vascular NO bioavailability. Immunohistochemistry suggested an increased venular adhesion marker expression (ICAM‐1, VCAM‐1) associated with the loss of NO bioavailability and this contributed significantly to a reduction in microvessel density. Taken together, these data begin to provide a temporally relevant, predictive model for poor microvascular outcomes in the metabolic syndrome. (NIH R01 DK64668, AHA EIA 0740129N)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".