Permeability Measures Predict Hemorrhagic Transformation after Ischemic Stroke
Bibliographic record
Abstract
Objective We sought to examine the diagnostic utility of existing predictors of any hemorrhagic transformation (HT) and compare them with new perfusion imaging permeability measures in ischemic stroke patients receiving alteplase only. Methods A pixel‐based analysis of pretreatment CT perfusion (CTP) was undertaken to define the optimal CTP permeability thresholds to predict the likelihood of HT. We then compared previously proposed predictors of HT using regression analyses and receiver operating characteristic curve analysis to produce an area under the curve (AUC). We compared AUCs using χ2 analysis. Results From 5 centers, 1,407 patients were included in this study; of these, 282 had HT. The cohort was split into a derivation cohort (1,025, 70% patients) and a validation cohort (382 patients or 30%). The extraction fraction (E) permeability map at a threshold of 30% relative to contralateral had the highest AUC at predicting any HT (derivation AUC 0.85, 95% confidence interval [CI], 0.79–0.91; validation AUC 0.84, 95% CI 0.77–0.91). The AUC improved when permeability was assessed within the acute perfusion lesion for the E maps at a threshold of 30% (derivation AUC 0.91, 95% CI 0.86–0.95; validation AUC 0.89, 95% CI 0.86–0.95). Previously proposed associations with HT and parenchymal hematoma showed lower AUC values than the permeability measure. Interpretation In this large multicenter study, we have validated a highly accurate measure of HT prediction. This measure might be useful in clinical practice to predict hemorrhagic transformation in ischemic stroke patients before receiving alteplase alone. ANN NEUROL 2020;88:466–476
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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 | 0.001 | 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 | 0.000 |
| 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 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".