Lp(a) as a Risk Factor for Peripheral Artery Disease: Context Is Everything
Bibliographic record
Abstract
Elevated plasma concentrations of Lp(a) (lipoprotein(a)) are an independent and causal risk factor for the development of atherosclerotic cardiovascular diseases, including peripheral artery disease (PAD). Although proatherosclerotic, proinflammatory, procalcific, and prothrombotic effects have been attributed to Lp(a), the precise pathogenic mechanisms by which Lp(a) contributes to these disorders are unclear. Moreover, whether Lp(a) contributes in different ways to atherosclerotic cardiovascular diseases in different vascular sites has not been explored. In particular, PAD involves atherosclerotic plaque rupture and subsequent thrombosis in vessels above the knee, but medial arterial calcification leading to vessel stiffness and thrombosis below the knee; the significance of Lp(a) in these contexts is unclear. Elevated Lp(a) is associated with the spectrum of PAD outcomes, including incident claudication, PAD progression, lower limb revascularization, restenosis, major adverse leg events, including limb amputation, and death and hospitalization due to PAD. Overall, elevated Lp(a) is as potent a risk factor for PAD as it is for coronary artery disease. Reducing Lp(a) to mitigate risk of PAD and to treat patients with PAD, therefore, remains a substantial unmet clinical need, although studies are underway to assess the efficacy of RNA-directed Lp(a)-lowering therapies in preventing atherosclerotic cardiovascular disease events. Mounting clinical trials of these therapies to specifically address their effect on PAD events is the next key step.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".