Investigating tissue factor pathway inhibitor and other protease and protease inhibitors and their association with major adverse aortic events in patients with abdominal aortic aneurysm
Bibliographic record
Abstract
Background: Abdominal aortic aneurysm (AAA) is characterized by the proteolytic breakdown of the extracellular matrix, leading to dilatation of the aorta and increased risk of rupture. Biomarkers that can predict major adverse aortic events (MAAEs) are needed to risk stratify patients for more rigorous medical treatment and potential earlier surgical intervention. Objectives: The primary objective was to identify the association between baseline levels of these biomarkers and MAAEs over a period of 5 years. Methods: Baseline levels of 3 proteases (matrix metalloproteinases 7, 8, and 10) and 3 protease inhibitors (tissue factor pathway inhibitor [TFPI], SerpinA12, SerpinB3) were investigated. Plasma levels of these biomarkers were quantified in 134 patients with AAA and 134 matched controls. Patients were followed for a 5-year period during which MAAEs were documented. The association between these markers and MAAEs was evaluated using Cox regression and Kaplan-Meier survival curves. Results: = .003) after adjusting for covariates. Kaplan-Meier survival analyses demonstrated that patients in the high TFPI group (defined as plasma levels >25.961 ng/mL) had significantly reduced freedom from the need for aortic repair and MAAEs. Conclusion: These findings suggest that TFPI may serve as a valuable prognostic marker for the risk of MAAEs within 5 years in patients with AAA, potentially offering new tools for the medical management of patients with AAA.
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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) | 0.000 | 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 | 0.000 |
| 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".