Factors Associated with Unfavorable Functional Outcomes After Intravenous Thrombolysis in Patients with Acute Ischemic Stroke
Bibliographic record
Abstract
Purpose: Intravenous thrombolysis (IVT) has become a standard treatment for eligible ischemic stroke patients. However, functional outcomes after receiving IVT varied widely. Hence the primary goal of this study is to identify characteristics related to poor outcomes. Patients and Methods: The study enrolled acute ischemic stroke patients aged 18 or older who received IVT within 4.5 hours after onset between January 2018 and December 2020. The data were retrospectively collected from medical records. The patients were classified as having an excellent (0-2) or poor (3-6) outcomes based on the 90-day modified Rankin Scale (mRS). Univariable and multivariable logistic regression analyses were used to evaluate the results. The predictive model was determined and developed the score using regression coefficients. The prediction power was validated using the area under the receiver operating characteristic curve analysis. Results: The study included 138 eligible participants. Forty-eight patients had unfavorable functional outcomes. With multivariable logistic regression analysis, factors significantly associated with poor outcomes were age (adjusted odds ratio (AOR), 1.03; 95% confidence interval (CI), 0.99-1.07; P = 0.05), diabetes (3.96; 1.61-9.37; P = 0.003), admission National Institute of Health Stroke Scale (NIHSS) (1.08; 1.01-1.15; P = 0.02) and initial Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (0.56; 0.37-0.86; P = 0.009). The predictive model developed from the findings demonstrated good discrimination power (AuROC 0.803, 95% CI 0.728-0.877). Conclusion: The current study found that older age, diabetes, atrial fibrillation, higher admission NIHSS, and lower ASPECTS on the initial NCCT brain were related to unfavorable functional outcomes following IVT and served as good predictors of patient functional 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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".