Abstract TMP87: A Novel Approach For Automated First Pass Effect Prediction On Computed Tomographic Angiography In Acute Ischemic Stroke Patients
Bibliographic record
Abstract
Background and Purpose: First pass effect (FPE), or effective reperfusion with a single pass of endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), is strongly associated with good functional outcome (mRS≤2) and can aid in optimal device selection for EVT. We aim to investigate the arterial tortuosity from great vessel origin to the intracranial occlusion location and predict first pass effect, automatically by a novel neural network, on computed tomographic angiography (CTA). Materials and Methods: Patients with thin-slice (≤2.5 mm) neck CTA from the ESCAPE-NA1 trial (Efficacy and safety of nerinetide for the treatment of AIS) were included. Arterial centerlines from the aortic arch to the intracranial occlusion site were manually traced by three experienced readers on CTA. The centerlines were modeled and organized by tortuosity. A model based on graph neural network (GNN) was proposed to predict FPE on the processed data. Model performance was compared to manual annotations and traditional classification algorithms using diagnostic statistics. Results: 539 patients were included, with a 429/110 training/testing-ratio. FPE was observed in 136(31.7%, training set) and 35 (31.8%, testing set). The suggested model showed an overall good performance with an area under the receiver operating characteristic curve (AUC-ROC) of 0.66 (95% CI=0.65-0.67), precision of 0.46(17/37), sensitivity of 0.71(17/35), specificity of 0.61(55/75) and accuracy of 0.65(72/110), whereas the manually annotated features with traditional classification methods had an AUC-ROC of 0.54 (95%CI=0.53, 0.55), precision of 0.42(8/19), sensitivity of 0.23(8/35), specificity of 0.85(64/75) and accuracy of 0.65(72/110). Conclusion: An automated model for FPE prediction on CTA had better performance and higher sensitivity as compared to manual annotations and traditional machine learning algorithms, suggesting that it could be used in routine clinical practice prior to EVT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".