Plasma complement component C2: a potential biomarker for predicting abdominal aortic aneurysm related complications
Bibliographic record
Abstract
Blood-based adjunctive measures that can reliably predict abdominal aortic aneurysm (AAA)-related complications hold promise for mitigating the AAA disease burden. In this pilot study, we sought to evaluate the prognostic performance of complement factors in predicting AAA-related clinical outcomes. We recruited consecutive AAA patients (n = 75) and non-AAA patients (n = 75) presenting to St. Michael's Hospital. Plasma levels of complement proteins were assessed at baseline, as well as prospectively measured regularly over a period of 2 years. The primary outcome was the incidence of rapidly progressing AAA (i.e. aortic expansion), defined as change in AAA diameter by either 0.5 cm in 6 months, or 1 cm in 12 months. Secondary outcomes included incidence of major adverse aortic events (MAAE) and major adverse cardiovascular events (MACE). All study outcomes (AAA diameter, MACE and MAAE) were obtained during follow-up. Multivariable adjusted Cox regression analyses were performed to assess the prognostic value of plasma C2 levels in patients with AAA regarding rapid aortic expansion and MAAE and MACE. Event-free survival rates of both groups were also compared. Compared to non-AAA patients, patients with AAA demonstrated significantly higher plasma concentrations of C1q, C4, Factor B, Factor H and Factor D, and significantly lower plasma concentrations of C2, C3, and C4b (p = 0.001). After a median of 24 months from initial baseline measurements, C2 was determined as the strongest predictor of rapid aortic expansion (HR 0.10, p = 0.040), MAAE (HR 0.09, p = 0.001) and MACE (HR 0.14, p = 0.011). Based on the data from the survival analysis, higher levels of C2 at admission in patients with AAA predicted greater risk for rapid aortic expansion and MAAE (not MACE). Plasma C2 has the potential to be a biomarker for predicting rapid aortic expansion, MAAE, and the eventual need for an aortic intervention in AAA patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".