Mediation of Multiphase Collateral Status on Functional Outcome by ASPECTS‐Based Net Water Uptake in Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSES: The Alberta Stroke Program Early CT Score-based net water uptake (ASPECTS-NWU) is a quantitative imaging biomarker used to assess early ischemic changes in acute ischemic stroke patients. ASPECTS-NWU has been investigated in identifying stroke onset time, measuring ischemic tissue edema, and predicting functional outcomes. However, the mediating effect of ASPECTS-NWU and its association with collaterals, infarct volume, and functional outcome still need to be explored. Therefore, we hypothesized that ASPECTS-NWU is a mediator between collateral circulation and infarct volume and investigated their association with outcome. METHODS: There were 201 patients, and 131 of them underwent mechanical thrombectomy. Collaterals were graded using the multiphase Menon score. The mediating effect of ASPECTS-NWU between collaterals and infarct volume was investigated. The association between infarct volume, collaterals, recanalization status, and functional outcome was assessed by univariable and multivariate logistic regression analysis. RESULTS: ), and smaller infarct volume, ischemic tissue volume, and penumbra volume. ASPECTS-NWU was a mediator between collaterals and infarct volume, and the contribution rate of the mediator was 27.9%. In multivariate logistic regression analysis, infarct volume and recanalization status were associated with functional outcomes. CONCLUSIONS: ASPECTS-NWU was a mediator and played a partial role between collaterals and infarct volume. Infarct volume and recanalization status were strong predictors of functional outcome. ASPECTS-NWU and collaterals indirectly influenced functional outcomes by regulating infarct volume.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".