Abstract TP177: Functional Outcome Prediction in Primary Angiitis of the Central Nervous System
Bibliographic record
Abstract
Introduction: Predicting the probability of functional independence in patients with primary angiitis of the central nervous system (PACNS) may help guide treatment decisions and prognostication. We sought to develop a prediction model for functional independence in adults with PACNS. Methods: Adults with PACNS were included from an international, multicenter, retrospective, observational cohort (COVAC). Clinical and imaging variables were collected prior to immunosuppressant treatment. Univariable and multivariable logistic regression models were used to identify baseline variables associated with functional independence (modified Rankin Score 0-2), measured 12 months after diagnosis. Results: Among the 194 patients included, 60 (31%) were diagnosed with PACNS based on a positive biopsy. At 12 months, 124 (65%) patients were functionally independent and 12 (6%) were dead. In multivariable logistic regression models, variables predictive of functional independence at 12 months were presence of ≥ 1 intracranial stenosis on CT- or MR-angiogram (aOR 3.33, 95% CI: 1.34-8.77, p=0.01), headache (aOR 2.66, 95% CI: 1.29-5.62, p<0.01), and absence of an altered level of consciousness (aOR 0.09, 95% CI: 0.02-0.28, p<0.001), an acute brain infarct (aOR 0.13, 95% CI: 0.04-0.34, p<0.001), or cognitive impairment (aOR 0.32, 95% CI: 0.15-0.68, p<0.01). A predictive model including these five variables showed good discrimination (c-statistic: 0.79, 95% CI: 0.72-0.86). Purely lymphocytic PACNS (n=34) was not associated with higher odds of functional independence than granulomatous or necrotizing PACNS (n=26) (OR 0.97, 95% CI: 0.33-2.83, p=0.96). Conclusions: This study identified baseline clinical and imaging variables that may predict functional independence in adults with PACNS. Validation of these results is required in an independent cohort.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".