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 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 | 0.057 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".