Data‐Driven Clustering Approach to Identify Different Phenotypes of Primary Central Nervous System Vasculitis
Bibliographic record
Abstract
BACKGROUND: To determine whether hierarchical unsupervised cluster analysis identifies a phenotypic distinction in adult patients with primary CNS vasculitis (PCNSV). METHODS: An agglomerative hierarchical cluster analysis based on the Ward method was conducted, including 153 patients with complete baseline phenotypic characterization in the COVAC' registry. RESULTS: The hierarchical analysis identified two main clusters. In Cluster 1 (n = 109 patients, 71%), patients more frequently had a motor deficit (p = 0.039), ≥ 1 acute brain infarct (p < 0.001), and ≥ 1 intracranial stenosis on CT or MR angiogram (p < 0.001) than patients in Cluster 2 (n = 44 patients, 29%). Conversely, patients in Cluster 2 more frequently had seizures (p < 0.001), cognitive impairment (p = 0.002), gadolinium-enhanced parenchymal lesions (p < 0.001), leptomeningeal enhancement (p < 0.001), ≥ 1 cerebral microbleed (p < 0.001), and intracranial hemorrhage(s) (p < 0.001). In multivariable logistic regression, gadolinium-enhanced parenchymal lesions were significantly associated with Cluster 2 lesions (OR = 35.53 [95% CI: 3.91-322.81], p = 0.002). Conversely, ≥ 1 acute brain infarct was significantly associated with Cluster 1 (OR = 0.003 [95% CI: 0.01-0.03], p < 0.001). A CNS biopsy was positive in 11/40 (28%) patients from Cluster 1 and 35/37 (95%) patients from Cluster 2 (p < 0.001). At 12 months, functional independence (modified Rankin scale score ≤ 2) did not differ between the two groups (p = 0.17). Relapse and mortality rates did not differ between the clusters (p = 0.17 and p = 0.23, respectively). CONCLUSION: This unsupervised analysis of a large PCNSV cohort identified two different clinical and radiological phenotypes with different diagnostic work-ups, which confirms the relevance of distinguishing PCNSV phenotypes according to the sizes of affected vessels.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".