Bibliographic record
Abstract
Only a few well-described cohorts of patients with primary CNS vasculitis (PCNSV; sometimes called primary angiitis of the CNS [PACNS]) have been published with enough follow-up to be able to really assess global and neurologic outcomes. 1,2There is still no definitive clinical tool or biomarker to confirm the diagnosis of PCNSV.The commonly used 1988 diagnostic criteria from Calabrese and Malek, slightly modified in 2009 by Birnbaum and Hellmann, have not been thoroughly validated, but made sense and matched clinical practice and diagnostic reasoning. 3A brain and/or leptomeningeal biopsy is the only way to definitively confirm CNS vasculitis, but a plethora of more frequent mimickers and causes for secondary CNS vasculitis have still to be ruled out, especially in patients with a diagnosis based on angiographic abnormalities and/or without obvious inflammatory CNS features on CSF analysis, gadolinium-enhancing lesions, or vessel wall enhancement on MRI. 3 A follow-up helps support the diagnosis, but possibly in the future, a minority of patients in these cohorts will be found to have a new genetic or infectioustriggered CNS condition.The list of PCNSV mimickers and secondary CNS vasculitis has been increasing steadily, with the identification of new genetically determined vasculopathies, such as deficiency in adenosine deaminase 2, and more sensitive tools, such as metagenomic nextgeneration sequencing, to detect rare CNS infections. 4 Hence, in real-world practice, as in the main published cohorts of PCNSV, only 30%-50% of reported patients had a brain biopsy, and only 50%-65% of the latter had biopsy-proven CNS vasculitis. 1,2Additional information on the main subsets of PCNSV that have been described is also needed to better determine their respective outcomes and, in clinical practice, how best to treat them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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; both teacher heads agree on what is shown here.
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".