Evaluation of Conventional Automated and Volume Weighted Automated Aspects vs. CT Perfusion Core Volume to predict the Final Infarct Volume after Successful Endovascular Therapy
Bibliographic record
Abstract
Objective: Comparing automated conventional and volume weighted Alberta Stroke Program Early CT score (ASPECTS) to CT perfusion core volume in order to predict the final infarct volume (FIV) in acute ischemic stroke (AIS) patients after successful thrombectomy. Materials and methods: Patients with AIS and large vessel occlusion who achieved TICI 2b or 3 reperfusion grade were included. Automated conventional and volume weighted ASPECT scores of the baseline CT were determined with e-ASPECTS software (Brainomix, Oxford, UK). Additionally, we used RAPID software (iSchemaView, Stanford, USA) to analyze the CT perfusion core volume. Results: We included 119 patients. Mean? SD values for automated conventional ASPECTS, volume weighted ASPECTS, CT perfusion core volume and FIV were 6.4? 2.6, 16.4 mL? 15.4, 18.3 mL? 24.6 and 70.0? 99.6. CTP core showed a higher correlation with FIV r = 0.4 (CI 95% 0.293; 0.497, P < 0.0001) than automated conventional ASPECTS (r =-0.209, CI 95% -0.323; -0.089, P = 0.002) and volume weighted ASPECTS (r = 0.185 CI 95% 0.065; 0.300, P = 0.003). Conclusion: In the setting of successful thrombectomy, CTP core volume is a better predictor of FIV than either automated conventional or volume weighted ASPECTS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".