Abstract TP140: Association Between Optimal Mismatch Ratio And Favorable Outcome By ASPECTS And Ischemic Core Volume
Bibliographic record
Abstract
Purpose: We aimed to clarify the association between optimal mismatch ratio and favorable outcome of acute ischemic stroke-large vessel occlusion (AIS-LVO) patients who underwent endovascular therapy (EVT) by Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) and ischemic core volume (ICV). Methods: We enrolled consecutive patients from 2017 to 2021 with prestroke modified Rankin scale (mRS) scores of 0 to 2 who were available for computed tomography perfusion or perfusion-weighted imaging before treatment and underwent EVT for anterior AIS-LVO within 24 hours from onset. Patients with ICV less than 10 mL or those who did not achieve successful recanalization with extended Thrombolysis In Cerebral Infarction scale score ≥2b were excluded. We dichotomized patients by ASPECTS (≥6 and <6), and then by ICV (≤70 mL and >70 mL). Sensitivity and specificity were calculated from receiver operating characteristic (ROC) curve and to identify the optimal mismatch ratio for achieving favorable outcome, defined as mRS score 0 to 2 at 3 months. Results: Eighty patients (women, 31; median age, 75 [interquartile range (IQR), 69-83] years; median NIHSS score, 19 [14-24]; median ASPECTS, 7 (IQR, 6-9); median ICV, 32 (IQR, 16-64) mL] were enrolled. Of these, 45 (56%) patients had favorable outcomes. The threshold of optimal mismatch ratio for favorable outcomes were 11.2 in patients with ASPECTS ≥6 [area under curve (AUC) 0.55, P=0.47; sensitivity 0.25, specificity 0.92], 3.1 in those with ASPECTS <5 (AUC 0.36, P=0.43; sensitivity 0.40, specificity 0.70), 11.2 in those with ICV <70 mL (AUC 0.52, P=0.84; sensitivity 0.24, specificity 0.92), and 3.6 in those with ICV ≥71 mL (AUC 0.55, P=0.82; sensitivity 0.50, specificity 0.90). Conclusions: The cut-off values of optimal mismatch ratio for favorable outcomes were 11 in patients with ASPECTS ≥6 or ICV <70 mL, and approximately 3 in those with large ICV.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".