Can CT perfusion accurately assess infarct core?
Bibliographic record
Abstract
We sought to quantify CTP-derived infarct core applying previously published perfusion thresholds to multi-institutional CTP data to assess the margin of error for 25 mL and 70 mL critical volume thresholds using early DWI as a reference standard. 60 patients with acute ischemic stroke undergoing CTP and DWI within 6 and 24 h of symptom onset, respectively, were retrospectively analyzed from 3 tertiary care centers. CTP-derived infarct core was calculated using published thresholds for absolute and relative CBF and CBV in addition to manual CBV tracing. Using DWI as the reference standard, performance of CTP-derived measures of infarct core was assessed using co-registered voxel-by-voxel analysis and total infarct volume comparison. Volumes of each CTP infarct core estimate were compared against DWI to determine the degree of infarct core over or underestimation at the critical volumes of 25 mL and 70 mL. Median core infarct volume was 10.8 mL. Mean CTP-derived infarct core volumes were similar to DWI for all CTP threshold methods to within ± 1 mL. CBV tracing demonstrated an overall significant core overestimation compared to DWI ( p = 0.017). All CTP core volume estimations showed robust correlation with DWI (Pearson p -value < 0.001). As core volume increased, CTP demonstrated increased deviation from DWI. At the critical cut-offs of 25 mL and 70 mL, relative CBF demonstrated the best agreement with DWI for infarct core compared to the other CTP-derived measures of infarct core. Our study demonstrates close approximation between multiple CTP-derived measures of infarct core and DWI infarct volume, Especially relative CBF.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".