Risk Factors and Prognostic Models in Acute Large Vessel Occlusion Stroke: Insights From ASPECTS‐Net Water Uptake
Bibliographic record
Abstract
BACKGROUND: The outcomes of endovascular reperfusion in acute large vessel occlusion stroke (ALVOS) vary, with some patients recovering fully while others face disability or mortality despite recanalization. Alberta stroke program early CT score-net water uptake (ASPECTS-NWU), a quantitative imaging metric assessing tissue edema and infarct progression, may improve prognostic accuracy. METHODS: This study included 96 ALVOS patients between December 2020 and March 2024. Patients were categorized into good (modified Rankin Scale [mRS] 0-2) and poor (mRS 3-6) prognosis groups based on 90-day mRS outcomes. Feature selection using Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, and logistic regression (LR) identified key predictors, including LVO, Alberta Stroke Program Early CT Score (ASPECTS), ASPECTS from the follow-up CT (ASPECTSFCT), and National Institutes of Health Stroke Scale (NIHSS) scores. Predictive performance was validated with cross-validation, and model calibration was assessed via calibration curves, receiver operating characteristic (ROC) curves, area under the curve (AUC), and the Spiegelhalter Z-test (significance set at p < 0.05). RESULTS: LR highlighted LVO, ASPECTS, ASPECTSFCT, and NIHSS as significant predictors of poor prognosis. The constructed nomogram enables individualized risk assessment, with total points correlating to poor outcome probability. ROC analysis showed good discriminatory ability in the training set (AUC 0.815, 95% confidence interval [CI]: 0.714-0.916) but moderate performance in the test set (AUC 0.688, 95% CI: 0.484-0.891). Calibration was strong in the training set (Spiegelhalter Z < 0.0001) but showed minor issues in the test set (Spiegelhalter Z = 1.222). CONCLUSIONS: This study highlights the prognostic value of ASPECTS-NWU in ALVOS and its integration into a predictive nomogram for individualized risk assessment. By refining ischemic injury stratification, ASPECTS-NWU can guide therapeutic decisions, optimize post-reperfusion management, and improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".