Computed tomography-based triage of extensive baseline infarction: ASPECTS and collaterals versus perfusion imaging for outcome prediction
Bibliographic record
Abstract
BACKGROUND: Patients presenting with large baseline infarctions are often excluded from mechanical thrombectomy (MT) due to uncertainty surrounding its effect on outcome. We hypothesized that computed tomography perfusion (CTP)-based selection may be predictive of functional outcome in low Alberta Stroke Program Early CT Score (ASPECTS) patients. METHODS: This was a double-center, retrospective analysis of patients presenting with ASPECTS≤5 who received multimodal admission CT imaging between May 2015 and June 2020. The predicted ischemic core (pCore) was defined as a reduction in cerebral blood flow (rCBF), while mismatch volume was defined using time to maximum (Tmax). The pCore perfusion mismatch ratio (CPMR) was also calculated. These parameters (pCore, mismatch volume, and CPMR), as well as a combined radiological score consisting of ASPECTS and collateral status (ASCO score), were tested in logistic regression and receiver operating characteristic (ROC) analyses. The primary outcome was favorable modified Rankin Scale (mRS) at discharge (≤3). RESULTS: A total of 113 patients met the inclusion criteria. The median ischemic core volume was 74.1 mL (IQR 43.8-121.8). The ASCO score was associated with favorable outcome at discharge (aOR 3.7, 95% CI 1.8 to 10.7, P=0.002), while no association was observed for the CTP parameters. A model including the ASCO score also had significantly higher area under the curve (AUC) values compared with the CTP-based model (0.88 vs 0.64, P=0.018). CONCLUSIONS: The ASCO score was superior to the CTP-based model for the prediction of good functional outcome and could represent a quick, practical, and easily implemented method for the selection of low ASPECTS patients most likely benefit from MT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".