Stepwise improvement in intracerebral haematoma expansion prediction with advanced imaging: a comprehensive comparison of existing scores
Bibliographic record
Abstract
BACKGROUND: We aim to comprehensively assess and compare the predictive performance of haematoma expansion (HE) scores in a homogeneous cohort of acute intracerebral haemorrhage (ICH) patients. METHODS: Existing scores for predicting HE in acute ICH patients were included and categorised by imaging modality: non-contrast CT (NCCT), single-phase CT angiography (sCTA) and multiphase CTA (mCTA). The predictive performance of the scores was evaluated with the c-statistic in a population of consecutive adult patients with acute ICH admitted to a tertiary care centre in Southern Alberta, Canada, between February 2012 and May 2020, investigated with a multimodal imaging protocol (NCCT, sCTA and mCTA). The primary outcome was HE (ICH volume growth ≥6 mL or ≥33%), and the secondary outcome was severe HE (ICH volume growth ≥12.5 mL or ≥66%). The DeLong test compared the best-performing scores from each imaging category. RESULTS: 16 HE scores were assessed (NCCT=8, sCTA=6 and mCTA=2) in 217 patients with a median age of 70 years (IQR=60-80), and 86 (39.6%) were females. 51 (23.5%) patients experienced HE and 35 (16.1%) had severe HE. The c-statistic for predicting HE ranged from 0.516 to 0.674 for NCCT-based scores, 0.627 to 0.725 for sCTA-based scores and 0.800 to 0.814 for mCTA-based score. The c-statistic for predicting severe HE ranged from 0.505 to 0.666 for NCCT scores, 0.651 to 0.740 for sCTA scores and 0.813 to 0.828 for mCTA scores. A statistically significant difference favouring mCTA over other imaging modalities in predicting both HE and severe HE was observed. CONCLUSIONS: Advanced imaging demonstrated a stepwise improvement in the predictive performance of HE scores. However, no existing score achieved excellent predictive performance (c-statistics ≥0.90) in our cohort, highlighting the need for further refinement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".