Accuracy of computed tomography perfusion-defined ischemic core and follow-up infarction after basilar artery thrombectomy
Bibliographic record
Abstract
Purpose Accurate identification of computed tomography (CT) perfusion ischemic core in patients with basilar artery occlusion (BAO) on admission remains challenging despite its critical role in prognostic prediction and treatment decision-making. We aimed to define the optimal threshold for identifying the ischemic core by assessing agreement in lesion extent and spatial distribution using Syngo.via. Methods We retrospectively analyzed 91 patients with BAO who achieved successful recanalization after endovascular thrombectomy at our center. The ischemic core was estimated using the following thresholds: cerebral blood flow (CBF) < 10 or 15 mL/100 g/min by Syngo.via, cerebral blood volume < 1.2 mL/100 mL by Syngo.via, and time to maximum > 10 s by RAPID. The Posterior Circulation Alberta Stroke Program Early CT Score was used to assess the extent of the infarction. Statistical analyses included the intraclass correlation coefficient (ICC) and receiver operating characteristic analyses. Results The CBF < 10 mL/100 g/min threshold demonstrated good agreement in extent with follow-up infarction (ICC: 0.81 [95% confidence intervals 0.72–0.87]), with overestimation or underestimation being the most uncommon ( n = 9). For the detection of midbrain, pontine, and cerebellar infarction, this threshold yielded the best performance with the area under the curve ranging from 0.79 (midbrain, 0.66–0.93; p < 0.001) to 0.90 (pons, 0.83–0.98; p < 0.001). Conclusion In patients with BAO after successful recanalization, the optimal threshold for the ischemic core was a CBF < 10 mL/100 g/min. This threshold may serve as a reliable imaging biomarker, aiding in the prediction of tissue outcomes and treatment decision-making.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".