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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".