Identification and Quantification of Precursory Changes of Rheumatoid Vasculitis in the Dorsalis Pedis Artery
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is a systemic connective tissue autoimmune disease that can infiltrate arterial walls. The delay in diagnosis and treatment of rheumatoid vasculitis (RV) in patients with RA may lead to irreversible damage to the arterial walls of small-to-medium vessels, which has serious and devastating consequences, most notably lung and cardiac damage. In this work an ultrasound image-based biomarker was developed to detect precursory changes in RV. METHODS: The ground truth was initiated from a medical diagnosis of RA, with arterial wall thickening of the proximal dorsalis pedis artery (DPA) indicating precursory changes of RV identified with ultrasound scanning. Ultrasound images of the DPA from 49 healthy subjects in the control group and 46 patients in the RA group were obtained. In total, 187 texture features were extracted from the images, followed by principal component analysis and linear discriminant analysis. RESULTS: ) with an area under the receiver operating characteristic curve of 0.85. Ten major textural features contributing most heavily to the biomarker were identified, with these textures being consistent with clinical observations of RV identified in previous studies. Interscan reproducibility was assessed by computing the biomarker twice based on repeated scans of each ankle. High interscan reproducibility was demonstrated by a strong and significant Pearson's coefficient (r = 0.85, p < 0.01) between the two repeated measurements of the proposed biomarker. CONCLUSION: The proposed biomarker can discriminate image textural differences seen in images acquired from RA patients, demonstrating precursory changes in RV compared with healthy controls. The major discriminative features identified in this study may facilitate the early identification and treatment of RV.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".