Bibliographic record
Abstract
Diabetes has profound effects on the vasculature, and the major complications of diabetes, accounting for increases in both morbidity and mortality, are diseases of the vasculature. In recent years, a number of techniques have been developed that provide direct in vivo assessments of vascular health in humans. These techniques include measurements of atherosclerotic burden, such as calculations of coronary calcium scores using computed tomography and high-resolution ultrasound measurements of the intima-media thickness of the carotid arteries. Tests of the dynamic properties of the vasculature have most prominently assessed endothelium-dependent vasodilation, using brachial artery ultrasound or more invasive measures such as thermodilution, dye dilution, or plethysmography. Another set of tools has been developed to assess the dynamic physical properties of the vascular tree. Although generally categorized as measures of “stiffness,” these techniques can in fact provide information on a number of specific physical properties, including distensibility, elasticity, and resistance to deformation. These parameters are different aspects of the interrelated features of vessel wall thickness, change in wall thickness, and vessel diameter in response to force and the rates of these changes as well as the rate of return to the nondeformed state. Abnormalities in these parameters can be demonstrated in tissues from diabetic subjects (1–3), and they can also be demonstrated in vivo. The most rigorous measurements of these features necessarily include a measurement of the distending force (i.e., blood pressure), ideally at the site of measurement. This can be achieved in the research setting with invasive techniques that place pressure …
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".