Comparison of patients with biopsy positive and negative primary angiitis of the central nervous system
Bibliographic record
Abstract
OBJECTIVE: There is limited evidence on when to obtain a central nervous system (CNS) biopsy in suspected primary angiitis of the central nervous system (PACNS). Our objective was to identify which clinical and radiological characteristics were associated with a positive biopsy in PACNS. METHODS: From the multicentre retrospective Cohort of Patients with Primary Vasculitis of the CNS (COVAC), we included adults with PACNS based on a positive CNS biopsy or otherwise unexplained intracranial stenoses with additional findings supportive of vasculitis. Baseline findings were compared between patients with a positive and negative biopsy using logistic regression models. RESULTS: Two hundred patients with PACNS were included, among which a biopsy was obtained in 100 (50%) and was positive in 61 (31%). Patients with a positive biopsy were more frequently female (odds ratio [OR] 2.90; 95% CI: 1.25, 7.10; P = 0.01) and more often presented with seizures (OR 8.31; 95% CI: 2.77, 33.04; P < 0.001) or cognitive impairment (OR 2.58; 95% CI: 1.11, 6.10; P = 0.03). On imaging, biopsy positive patients more often had non-ischaemic parenchymal or leptomeningeal gadolinium enhancement (OR 52.80; 95% CI: 15.72, 233.06; P < 0.001) or ≥1 cerebral microbleed (OR 8.08; 95% CI: 3.03, 25.13; P < 0.001), and less often had ≥1 acute brain infarct (OR 0.02; 95% CI: 0.004, 0.08; P < 0.001). In the multivariable model, non-ischaemic parenchymal or leptomeningeal gadolinium enhancement (adjusted OR 8.27; 95% CI: 1.78, 38.46; P < 0.01) and absence of ≥1 acute brain infarct (adjusted OR 0.13; 95% CI: 0.03, 0.65; P = 0.01) were significantly associated with a positive biopsy. CONCLUSION: Baseline clinical and radiological characteristics differed between biopsy positive and negative PACNS. These results may help physicians individualize the decision to obtain a CNS biopsy in suspected PACNS.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".