Abstract TP43: Delayed Phase Blood Pool Assessment of Multiphase-CTA in Acute Stroke Provides Further Information Beyond Non-Contrast CT
Bibliographic record
Abstract
Introduction: Multiphase CT-Angiography (mCTA) can provide dynamic parenchymal hemodynamic information, including proper microvascular ‘blood pool’ assessment. Our objective was to determine which ASPECTS scoring method between non-contrast CT (NCCT), and delayed phase blood pool assessment on mCTA best predicted final infarct. This can contribute to decision making for endovascular therapy. Hypothesis: ASPECTS on delayed phase blood pool analysis would best predict final infarct score. Methods: Patients with TICI 0, 2b, or 3 reperfusion who met criteria for thrombolysis and endovascular therapy were included. ASPECTS scores were calculated on admission NCCT. Blood pool ASPECTS were calculated as discrete areas of hypodensity on delayed phase mCTA. Baseline scores were compared to final infarct score on follow-up CT/MRI. Sensitivities and specificities were calculated and then stratified by brain region to determine the most predictive method. Results: Fifty-three patients were analyzed (25/53= TICI 0, 10/53= TICI 2b, 15/53= TICI 3). Delayed phase blood pool mCTA showed similar sensitivities for ganglionic regions and improved sensitivities for supraganglionic regions. Delayed phase blood pool analysis of mCTA showed improved specificity in ganglionic regions over NCCT (Table 1, Figure 1). Conclusion: In conclusion, blood pool assessment on delayed phase mCTA has the capability to identify additional at risk tissue beyond that defined by NCCT.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".