Clinical Implications of Prominent Cortical Vessels on Susceptibility-Weighted Imaging in Acute Ischemic Stroke Patients Treated with Recanalization Therapy
Bibliographic record
Abstract
Prominent cortical vessels on susceptibility-weighted imaging (PCV–SWI) correlate with poor leptomeningeal collaterals. However, little is known about PCV–SWI in recanalization therapy-treated patients with anterior circulation large vessel occlusions (LVO). We investigated PCV–SWI-based assessment of leptomeningeal collaterals and outcome predictions in 100 such patients in an observational study. We assessed PCV–SWI using the Alberta Stroke Program Early CT Score and evaluated leptomeningeal collaterals on multiphase CT angiography (mCTA). Predictive abilities were analyzed using multivariable logistic regression and area of receiver operating curves (AUCs). The extent of PCV–SWI correlated with leptomeningeal collaterals on mCTA (Spearman test, r = 0.77; p < 0.001); their presence was associated with worse functional outcomes and a lower successful recanalization rate (adjusted odds ratios = 0.24 and 0.23, 95% CIs = 0.08–0.65 and 0.08–0.65, respectively). The presence of PCV–SWI predicted outcomes better than good collaterals on mCTA did (C-statistic = 0.84 vs. 0.80; 3-month modified Rankin Scale (mRS) 0–2 = 0.75 vs. 0.67 for successful recanalization). Comparison of AUCs showed that they had similar abilities for predicting outcomes (p = 0.68 for 3-month mRS 0–2; p = 0.23 for successful recanalization). These results suggest that PCV–SWI is a useful feature for assessing leptomeningeal collaterals in acute ischemic stroke patients with anterior circulation LVO and predicting outcomes after recanalization therapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".