A Causal Classification System for Intracerebral Hemorrhage Subtypes
Bibliographic record
Abstract
OBJECTIVE: Determining the underlying causes of intracerebral hemorrhage (ICH) is of major importance, because risk factors, prognosis, and management differ by ICH subtype. We developed a new causal CLASsification system for ICH Subtypes, termed CLAS-ICH, based on recent advances in neuroimaging. METHODS: CLAS-ICH defines 5 ICH subtypes: arteriolosclerosis, cerebral amyloid angiopathy, mixed small vessel disease (SVD), other rare forms of SVD (genetic SVD and others), and secondary causes (macrovascular causes, tumor, and other rare causes). Every patient is scored in each category according to the level of diagnostic evidence: (1) well-defined ICH subtype; (2) possible underlying disease; and (0) no evidence of the disease. We evaluated CLAS-ICH in a derivation cohort of 113 patients with ICH from Massachusetts General Hospital, Boston, USA, and in a derivation cohort of 203 patients from Inselspital, Bern, Switzerland. RESULTS: In the derivation cohort, a well-defined ICH subtype could be identified in 74 (65.5%) patients, including 24 (21.2%) with arteriolosclerosis, 23 (20.4%) with cerebral amyloid angiopathy, 18 (15.9%) with mixed SVD, and 9 (8.0%) with a secondary cause. One or more possible causes were identified in 42 (37.2%) patients. Interobserver agreement was excellent for each category (kappa value ranging from 0.86 to 1.00). Despite substantial differences in imaging modalities, we obtained similar results in the validation cohort. INTERPRETATION: CLAS-ICH is a simple and reliable classification system for ICH subtyping, that captures overlap between causes and the level of diagnostic evidence. CLAS-ICH may guide clinicians to identify ICH causes, and improve ICH classification in multicenter studies. ANN NEUROL 2023;93:16-28.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".