Evaluating Hematoma Expansion Scores in Acute Spontaneous Intracerebral Hemorrhage
Bibliographic record
Abstract
Background and Purpose- In acute spontaneous intracerebral hemorrhage, multiple hematoma expansion scores have been proposed for use in clinical trial environments. We performed a systematic scoping review to identify all existing hematoma expansion scores and describe their development, validation, and relative performance. Methods- Two reviewers searched MEDLINE, PUBMED, EMBASE, and CENTRAL (Cochrane Central Register of Controlled Trials) for studies that derived or validated a hematoma expansion prediction score in adults presenting with spontaneous intracerebral hemorrhage. A descriptive analysis of the extracted data was performed, focusing on score development techniques and predictive capabilities. Results- Of the 14 434 records retrieved, 15 studies met inclusion criteria and 10 prediction scores were identified. Validation analysis using independent samples was performed in 9 studies on 5 scores. All derivation studies reported high performance with C statistics ranging from 0.72 to 0.93. In validation, the C-statistic range was broader with studies reporting 0.62 to 0.77. For every score, the risk of expansion increased with each point increase, although patients with high scores were rare. Conclusions- At present, 10 hematoma expansion scores have been developed, of which 5 have been externally validated. Real-world performance in validation studies was lower than performance in derivation studies. Data from the current literature are insufficient to support a meaningful meta-analysis.
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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 | 0.001 |
| 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 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".