Abstract WMP23: Regional Assessment of Multi-phase CTA and CT Perfusion are Equivalent in Predicting Tissue Fate in Ischemic Stroke
Bibliographic record
Abstract
Introduction: The use of CT Perfusion (CTP) in acute ischemic stroke (AIS) to determine patients with large ischemic core is still hampered by slow processing time, among other technical/standardization issues. Multi-phase CTA (mCTA) may be quicker and more practical in this regard. We sought to determine i) the performance of mCTA and CTP to predict regional infarction and ii) which mCTA construct(s) corresponds to which CTP parameter . Methods: mCTA and CTP was performed less than 12hrs from ictus in 77 patients with MCA-M1 occlusions. Regional analysis was performed within M2-M6 ASPECTS-regions. mCTA: regional pial vessels were assessed according to three constructs: i) Delay in maximal pial vessel enhancement compared to contralateral hemisphere; ii) Washout of contrast within pial vessels; iii) Extent of maximal pial vessel enhancement compared to contralateral hemisphere (Figure 1). CTP-CBF, CBV, MTT, IRF-T0, and Tmax values were determined. 24-hour MR-DWI or NCCT was used for final infarction. Results: There was a negligible difference in the predictive accuracy of mCTA and CTP in discriminating infarction (i.e., 84.59% and 83.04%, respectively). mCTA-Extent had the largest discriminatory power, while CTP-Tmax had the largest discriminatory power. Conclusion: Herein we show that mCTA assessments, even within small brain regions can help determine tissue fate when adjusted for recanalization, and is as good as CTP. mCTA may be a more practical modality to obtain similar prognostic information for radiological and clinical outcomes in AIS, informing acute treatment and tertiary centre triaging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".