Abstract W MP24: Improving Collateral Circulation Evaluation Accuracy: Multiphase CTA on Acute Stroke
Bibliographic record
Abstract
Good collateral circulation (CC) associates favourable outcomes on acute stroke patients, but which is the best technique to evaluate it is controversial. Single-phase CTA (sCTA) is widely used, but lacks of temporal resolution, and may mislabel CC. We aim to evaluate a new, quick (not post processing), time resolved technique to evaluate CC: multiphase CTA (mCTA). METHODS: Consecutive <4.5h stroke patients evaluated for reperfusion therapies with confirmed M1-MCA or TICA occlusion by sCTA were included. Two more cerebral CTA acquisitions with 10 and 20 seconds delay were performed for mCTA. CC evaluation is described in the Table. sCTA and mCTA were compared as predictors of clinical, radiological and functional endpoints. Recanalization (REC) was assessed by TCD at 24h. RESULTS: 78 patients were included. Mean age: 66.3±13.6y, median NIHSS 17.5 (IQR 6.3), 52 (66.7%) M1- and 26 (33.3%) TICA-occlusions. Mean time from onset to CTA: 2:32±1:31h. On sCTA, 61.8% patients presented good CC whereas on mCTA, 54.7%. Only on mCTA good CC was an independent predictor of low infarct volume at 24h (OR 3.6, CI 95% 1.3-10.5, p=0.017). Moreover, only mCTA-CC status was associated with lower 24h median NIHSS (good CC:5 vs poor CC:17, p<0.001), and 3 months favourable outcome (mRS0-2: good CC 57.1% vs poor CC 11.5%, p<0.001). Association with outcome was especially significant in patients without REC: among poor CC patients, mRS0-2: 0% in non REC Vs 50% in REC (p<0.01). In a logistic regression model including age, NIHSS, ASPECTS and REC, only good CC on mCTA predicted favourable outcome (OR 6.8, CI 95% 1.6-29.2, p=0.009). CONCLUSION: CC evaluation on mCTA improves accuracy of clinical and radiological endpoints as compared with sCTA. Good CC on mCTA is an independent predictor of low infarct volume and good outcome, especially if REC is not achieved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".