CT Angiography Clot Burden Score and Collateral Score: Correlation with Clinical and Radiologic Outcomes in Acute Middle Cerebral Artery Infarct
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Clot extent, location, and collateral integrity are important determinants of outcome in acute stroke. We hypothesized that a novel clot burden score (CBS) and collateral score (CS) are important determinants of clinical and radiologic outcomes and serve as useful additional stroke outcome predictors. MATERIALS AND METHODS: One hundred twenty-one patients with anterior circulation infarct presenting within 3 hours of stroke onset were reviewed. The Spearman correlation was performed to assess the correlation between CBS and CS and clinical and radiologic outcome measures. Patients were dichotomized by using a 90-day modified Rankin scale (mRS) score. Uni- and multivariate logistic regression models were used to assess variables predicting favorable clinical and radiologic outcomes. Receiver operating characteristic and intraclass correlation coefficient (ICC) analyses were performed. Diagnostic performance of a CBS threshold of >6 was assessed. RESULTS: There were 85 patients (mean age, 70 +/- 14.5 years). Patients with higher CBS and CS demonstrated smaller pretreatment perfusion defects and final infarct volume and better clinical outcome (all, P < .01). CBS (P = .009) and recanalization (P = .015) independently predicted favorable outcome. A CBS >6 predicted good clinical outcome with an area under the curve of 0.75 (95% confidence interval [CI], 0.65-0.84; P = .0001), sensitivity of 73.0 (95% CI, 55.9-86.2), and specificity of 64.6 (95% CI, 49.5-77.8). The recanalization rate with intravenous recombinant tissue plasminogen activator was higher in patients with CBS >6 (P = .04; odds ratio, 3.2; 95% CI, 1.1-9.4). The ICC was 0.97 (95% CI, 0.95-0.98) and 0.87 (95% CI, 0.80-0.91) for CBS and CS, respectively. CONCLUSIONS: CBS and CS are useful additional markers predicting clinical and radiologic outcomes.
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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 | 0.000 |
| 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".