Abstract TP325: Lack of Early Improvement Predicts Poor Clinical Outcome Following Acute Intracerebral Hemorrhage
Bibliographic record
Abstract
Background: Early Neurological Worsening (ENW) is common after ICH, and predicts poor outcome. However, there is limited data as to what degree of ENW best relates to outcome. We used two ICH cohorts to refine and validate a definition of ENW that best predicted 90-day outcomes. Methods: We generated receiver operating characteristic (ROC) curves for the association between 24-hour NIHSS change and ICH outcomes using data from the VISTA collaboration. Primary outcome was poor outcome at 90 days (mRS 4-6); secondary outcomes were other mRS cutpoints (mRS 2-6, 3-6, 5-6, 6). We tested the commonly used NIHSS≥4 definition and in addition employed Youden’s J Index to select optimal cutpoints and calculated sensitivity, specificity, and predictive values. Independent predictors of poor outcome were determined via multivariable logistic regression. Definitions were validated in the prospectively collected PREDICT-ICH cohort. Results: Using 552 patients from the VISTA cohort, ROC curves of 24hr NIHSS change had an area under the curve of 0.75. NIHSS change of ≥0 at 24hrs was seen in 46.4%. Youden’s method showed an optimum cutoff at -0.5. Based on this, ENW defined as >0 (Sens 43%, Spec 91%, PPV 83%, aOR 7.13 [CI:4.05-12.55]), ≥0 (Sens 65%, Spec 73%, PPV 70%, aOR 5.05 [CI:3.25-7.85]), or ≥-1 (Sens 78%, Spec 59%, PPV 65%, aOR 6.04 [CI:3.75-9.71]) all accurately predicted poor outcome. PPV increased with higher NIHSS cutoffs, but at the cost of lower sensitivities. Regression confirmed that all definitions independently predicted outcome at all mRS cutpoints. ENW definitions reproduced well in the validation cohort of 275 patients. Conclusion: All NIHSS cut-offs for ENW predict clinical outcome, regardless of outcome definition. In particular, lack of clinical improvement at 24 hours (i.e. NIHSS is the same or higher) robustly predicted poor outcome, but may not be sufficiently reliable to determine clinical management.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".