Comparison of four different collateral scores in acute ischemic stroke by CT angiography
Bibliographic record
Abstract
PURPOSE: Multiple scores have been described for the assessment of collateralization in acute ischemic stroke. Currently, there is no gold standard for collateral assessment by CT angiography (CTA). This study compared four frequently used collateral scores with regard to their correlation with early infarct core and mismatch ratio. METHODS: 30 consecutive patients with acute occlusion of the M1 segment or terminal carotid artery were reviewed retrospectively. Collaterals were assessed using dynamic and also single-phase CTA according to grading systems by the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR), Alberta Stroke Program Early CT Score (ASPECTS) (on collaterals), Christoforidis et al and Miteff et al. The Christoforidis and ASITN/SIR scores, which were initially designed for conventional angiography, were adapted to be applicable to CTA. The scores were compared with respect to early infarct core and mismatch ratio in perfusion CT estimated by RAPID software using Spearman correlation. RESULTS: ASITN/SIR and ASPECTS collateral scores showed good correlation with early infarct core (rho=-0.696, p<0.001 and rho=-0.677, p<0.001) and mismatch ratio (rho=0.609, p<0.001 and rho=0.581, p<0.001). In contrast, the Christoforidis and Miteff scores correlated less well with infarct core (rho=0.245, p=0.191 and rho=-0.272, p=0.145, respectively) and mismatch ratio (rho=-0.329, p=0.075 and rho=0.279, p=0.135, respectively). ASPECTS and ASITN/SIR showed excellent cross-correlation (rho=0.901, p<0.001). CONCLUSIONS: Compared with the Christoforidis and Miteff scores, the modified ASITN/SIR and ASPECTS collateral scores showed consistently higher correlation with the extent of early infarct core and mismatch volume. This is probably because these scores evaluate the extent and delay of vascular enhancement in the affected territory rather than the backflow of contrast medium to the occlusion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".