Angiogenesis-related proteins as biomarkers for peripheral artery disease
Bibliographic record
Abstract
Background: Angiogenesis plays an important role in peripheral artery disease (PAD) and angiogenesis-related proteins may act as prognostic biomarkers. This study assesses the potential for angiogenesis-related proteins to predict adverse events associated with PAD. Methods: This was a case-control study. Patients with PAD (n = 250) and without PAD (n = 125) provided blood samples and were followed prospectively for three years. Concentrations of 17 angiogenesis-related proteins were measured in plasma. The incidence of major adverse limb event (MALE), defined as a composite of major amputation or vascular intervention, was the primary outcome. Worsening PAD status, defined as a drop in ankle brachial index ≥ 0.15, was the secondary outcome. Multivariable regression adjusted for baseline characteristics was conducted to determine the prognostication value of angiogenesis-related proteins in predicting MALE. Findings: Relative to patients without PAD, 8 proteins related to angiogenesis were expressed differentially in PAD patients. Worsening PAD status and MALE were observed in 52 (14%) and 83 (22%) patients, respectively. Hepatocyte growth factor (HGF) was the most reliable predictor of MALE (adjusted HR 0.79, 95% CI 0.15-0.86). Compared to individuals with high HGF, patients with low HGF had a decreased three-year freedom from MALE [66% vs 88%, p = 0.001], major amputation [93% vs 98%, p = 0.023], vascular intervention [68% vs 88%, p = 0.001], and worsening PAD status [81% vs 91%, p = 0.006]. Interpretation: Measuring plasma levels of HGF in individuals with PAD can assist in identifying patients at elevated risk of adverse events related to PAD who may benefit from additional evaluation or treatment.
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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 | 0.002 | 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 | 0.000 | 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".