Lumpers and Splitters: Ongoing Issues in the Classification of Large Vessel Vasculitis
Bibliographic record
Abstract
Large vessel vasculitis (LVV) is defined as inflammation that affects the aorta and its major branches. Takayasu arteritis (TAK) and giant cell arteritis (GCA) are the 2 main forms of LVV1. Historically, GCA is considered a disease of the elderly that targets the extracranial arteries (e.g., carotid and temporal arteries). In contrast, TAK typically affects younger patients and targets the aorta and its primary branches (e.g., subclavian and renal arteries). However, increasing evidence, primarily based on radiographic studies, demonstrates that vasculitic involvement of the aorta and primary branches can occur in GCA in addition to the more widely recognized cranial features of the disease2. Current estimates about the prevalence of involvement of the aorta and primary branches in GCA vary widely across different cohorts, but large vessel pathology is apparent by angiography in about 20%–30% of patients with GCA3,4. There are no existing guidelines regarding screening for large artery involvement in GCA, so the incidence of large vessel disease in GCA may be underestimated. An older necropsy study of 4 patients with GCA and known temporal arteritis, in which there was no clinical suspicion for vasculitis in the aorta and branch vessels, demonstrated widespread vasculitic lesions throughout the large arteries in every patient5. Cumulative incidence rates of large vessel involvement in GCA have increased dramatically over the last 2 decades in parallel with increased awareness about this feature of GCA6. Recognition of involvement of vessels beyond the extracranial arteries as a feature of GCA has created new challenges in the disease classification of LVV. The 1990 American College of Rheumatology (ACR) Classification Criteria for GCA were developed in a time when involvement of the aorta and primary branches was not a well-recognized feature of GCA and are focused … Address correspondence to Dr. P.C. Grayson, National Institutes of Health, 10 Center Drive, Building 10, 6N Rm 216G, Bethesda, Maryland 20892, USA. E-mail: peter.grayson{at}nih.gov
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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 | 0.074 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.028 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".