Perfusion-Weighted Imaging–Derived Collateral Flow Index is a Predictor of MCA M1 Recanalization after IV Thrombolysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recent studies highlight the role of CC in preserving ischemic penumbra. Some authors suggested the quality of CC could also impact recanalization. The purpose of this study is to test this hypothesis in patients who were treated with i.v. thrombolysis for MCA-M1 occlusion. MATERIALS AND METHODS: A normalized index derived from Tmax maps (MR-PWI) was defined to quantify the CC deficit (nCCD) in 64 patients with stroke who underwent i.v. thrombolysis. Correlations between nCCD and parameters that may be altered by CC quality were tested (baseline NIHSS, volume of diffusion abnormalities, modified Rankin Scale at 3 months). The correlation between baseline nCCD and MCA-M1 recanalization rate at 24 hours was tested. RESULTS: The nCCD is significantly correlated with NIHSS and with lesional volume (Pearson correlation test, positive correlations, respectively, 0.40, 0.57; P = .00089, P = 8.7e-07). The nCCD also has a significant predictive value on the full recanalization at 24 hours that decreases as TTT increases (logistic regression, P = .021). Furthermore, among patients who were treated within 3 hours, nCCD and recanalization are significantly correlated (correlation ratio test, eta2 = 0.23, P = .0023): Patients who did not achieve full recanalization have significantly higher nCCD than fully recanalized patients (Mann-Whitney U test, P = .007). In addition, the probability of full recanalization decreases as the nCCD increases (P = .021). nCCD (OR 0.988, 95% CI 0.977-0.999, P = .042) and full recanalization at 24 hours (OR 4.539, 95% CI 1.252-16.456, P = .021) are independent predictors of functional independence at 3 months. CONCLUSIONS: The nCCD index is a predictor of full MCA-M1 recanalization in patients treated with i.v. thrombolysis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".