The Role of Topographic Collaterals in Predicting Functional Outcome after Thrombolysis in Anterior Circulation Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: The Alberta Stroke Program Early CT (ASPECTS) leptomeningeal collaterals score on CT-angiography helps in prognosticating functional outcome in acute ischemic stroke (AIS) patients treated with intravenous thrombolysis. We evaluated whether a simplified topological ASPECTS collaterals scoring could serve as a rapid biomarker for early prediction in thrombolyzed AIS patients. METHODS: Consecutive patients from 2010 to 2014 with anterior circulation AIS treated with intravenous thrombolysis were included. The primary outcome was good functional outcome (modified Rankin scale score 0-1 at 3-months). Collaterals were scored according to the extent of contrast opacification in arteries distal to the acute occlusion. Prognostic value of individual ASPECTS leptomeningeal collateral regions was determined by multivariate logistic regression. RESULTS: A total of 283 patients were included (mean National Institutes of Health Stroke Scale [NIHSS] score 19.0 ± 6.3 points). Using multivariate logistic regression, good M5 region (parietal)-collaterals (OR 2.62, 95%CI 1.215-5.682, P = .014), younger age (OR .97 per year, 95%CI .943-.990, P = .006), nondiabetics (OR .44, 95%CI .224-.889, P = .021), and lower NIHSS (OR .89 per point, 95%CI .842-.935, P < .001) were independently associated with good functional outcome. The receiver operating characteristic curve showed NIHSS as a good predictor of functional outcome (area under the curve .718, 95%CI .656-.780, P < .001). However, a better predictive value was achieved when M5 collateral score was added to the NIHSS (area under the curve .752, 95%CI .694-.809, P < .001). CONCLUSIONS: Good collaterals in the M5 region are associated with good functional outcome. Addition of this simple neuroimaging tool to the pretreatment NIHSS may serve as a reliable biomarker for prognosis.
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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 | 0.003 |
| 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".