VEGF as a predictor of major adverse events in patients with peripheral arterial disease – an exploratory study
Bibliographic record
Abstract
Peripheral arterial disease (PAD) is an atherosclerotic disease caused by the narrowing of peripheral arteries due to atheromatous plaque build-up. Angiogenesis can be beneficial in patients with PAD however it may contribute to negative long-term cardiovascular outcomes. Vascular endothelial growth factor-A (VEGF/VEGF-A) has been associated with PAD. This study investigated the association between VEGF and major adverse cardiovascular events (MACE), and major adverse limb events (MALE) in patient with PAD. Plasma levels of VEGF were quantified in 300 patients with PAD (ABI < 0.9). Patients were monitored for MACE, defined as the composite of myocardial infarction, stroke, and cardiovascular-related death and MALE, defined as composite of progression to chronic limb threatening ischemia, need for surgical intervention, graft/stent re-occlusion, and need for minor or major limb amputation. Multivariable Cox proportional hazards regression was used to assess associations between VEGF levels and the risk of MACE and MALE, reported as hazard ratios with 95% confidence intervals. A Bonferroni correction was applied to address multiple hypothesis testing with p < 0.025 considered as significant. VEGF was independently associated with an increased risk of MACE (HR 1.26, 95% CI: 1.14-1.40, p < 0.001) and MALE (HR 1.176, 95% CI: 1.066-1.297, p = 0.001) after adjusting for cardiovascular risk factors. VEGF demonstrated a strong association with MACE and MALE events with each unit increase in VEGF being associated with a 26.0% increase in the risk of MACE and 17.6% increase in the risk of MALE, supporting its potential utility in risk stratification in patients with PAD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".