Pilot investigation of circulating angiogenic and inflammatory biomarkers associated with vascular malformations
Bibliographic record
Abstract
Abstract Background Vascular malformations in the central nervous system are difficult to monitor and treat due to their inaccessible location. Angiogenic and inflammatory proteins are secreted into the bloodstream and may serve as useful biomarkers for identifying patients at risk for complications or with certain disease phenotypes. Methods A validated multiplex protein array consisting of 26 angiogenic and inflammatory biomarkers (Angiome) was assessed in plasma isolated from healthy controls and patients with either sporadic brain arteriovenous malformation (BAVM), familial cerebral cavernous malformation (CCM), or hereditary hemorrhagic telangiectasia (HHT). These samples were obtained from archives of ongoing research studies at the University of California San Francisco and through prospective collection at the Toronto HHT Centre at St. Michael’s Hospital. Results We compared circulating biomarker levels from each patient group to healthy controls and analyzed each pairwise combination of patient groups for differences in biomarker levels. Additionally, we analyzed the HHT samples to determine the association between biomarker levels and the following HHT-specific phenotypes, BAVM, pulmonary arteriovenous malformation (PAVM), liver vascular malformation (LVM), and gastrointestinal (GI) bleeding. Compared to controls, levels of SDF1 were significantly elevated in HHT patients (Proportional Increase [PI] = 1.87, p
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".