Validation of the 9-Point and 24-Point Hematoma Expansion Prediction Scores and Derivation of the PREDICT A/B Scores
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Nine- and 24-point prediction scores have recently been published to predict hematoma expansion (HE) in acute intracerebral hemorrhage. We sought to validate these scores and perform an independent analysis of HE predictors. METHODS: We retrospectively studied 301 primary or anticoagulation-associated intracerebral hemorrhage patients presenting <6 hours post ictus prospectively enrolled in the Predicting Hematoma Growth and Outcome in Intracerebral Hemorrhage Using Contrast Bolus Computed Tomography (PREDICT) study. Patients underwent baseline computed tomography angiography and 24-hour noncontrast computed tomography follow-up for HE analysis. Discrimination and calibration of the 9- and 24-point scores was assessed. Independent predictors of HE were identified using multivariable regression and incorporated into the PREDICT A/B scores, which were then compared with existing scores. RESULTS: The 9- and 24-point HE scores demonstrated acceptable discrimination for HE>6 mL or 33% and >6 mL, respectively (area under the curve of 0.706 and 0.755, respectively). The 24-point score demonstrated appropriate calibration in the PREDICT cohort (χ2 statistic, 11.5; P=0.175), whereas the 9-point score demonstrated poor calibration (χ2 statistic, 34.3; P<0.001). Independent HE predictors included spot sign number, time from onset, warfarin use or international normalized ratio>1.5, Glasgow Coma Scale, and National Institutes of Health Stroke Scale and were included in PREDICT A/B scores. PREDICT A showed improved discrimination compared with both existing scores, whereas performance of PREDICT B varied by definition of expansion. CONCLUSIONS: The 9- and 24-point expansion scores demonstrate acceptable discrimination in an independent multicenter cohort; however, calibration was suboptimal for the 9-point score. The PREDICT A score showed improved discrimination for HE prediction but requires independent validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".