Calculation of Prognostic Scores, Using Delayed Imaging, Outperforms Baseline Assessments in Acute Intracerebral Hemorrhage
Bibliographic record
Abstract
Background and Purpose- Patients with intracerebral hemorrhage (ICH) are often subject to rapid deterioration due to hematoma expansion. Current prognostic scores are largely based on the assessment of baseline radiographic characteristics and do not account for subsequent changes. We propose that calculation of prognostic scores using delayed imaging will have better predictive values for long-term mortality compared with baseline assessments. Methods- We analyzed prospectively collected data from the multicenter PREDICT study (Prediction of Hematoma Growth and Outcome in Patients With Intracerebral Hemorrhage Using the CT-Angiography Spot Sign). We calculated the ICH Score, Functional Outcome in Patients With Primary Intracerebral Hemorrhage (FUNC) Score, and modified ICH Score using imaging data at initial presentation and at 24 hours. The primary outcome was mortality at 90 days. We generated receiver operating characteristic curves for all 3 scores, both at baseline and at 24 hours, and assessed predictive accuracy for 90-day mortality with their respective area under the curve. Competing curves were assessed with nonparametric methods. Results- The analysis included 280 patients, with a 90-day mortality rate of 25.4%. All 3 prognostic scores calculated using 24-hour imaging were more predictive of mortality as compared with baseline: the area under the curve was 0.82 at 24 hours (95% CI, 0.76-0.87) compared with 0.78 at baseline (95% CI, 0.72-0.84) for ICH Score, 0.84 at 24 hours (95% CI, 0.79-0.89) compared with 0.76 at baseline (95% CI, 0.70-0.83) for FUNC, and 0.82 at 24 hours (95% CI, 0.76-0.88) compared with 0.74 at baseline (95% CI, 0.67-0.81) for modified ICH Score. Conclusions- Calculation of the ICH Score, FUNC Score, and modified ICH Score using 24-hour imaging demonstrated better prognostic value in predicting 90-day mortality compared with those calculated at presentation.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".