Improving the Predictive Accuracy of Recanalization on Stroke Outcome in Patients Treated With Tissue Plasminogen Activator
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although early recanalization is a powerful predictor of stroke outcome after thrombolysis, some stroke patients remain disabled despite tissue plasminogen activator (tPA)-induced recanalization. Therefore, we sought to investigate whether the predictive accuracy of early recanalization on stroke outcome is improved when combined with clinical and radiological information. METHODS: We evaluated 177 patients with nonlacunar strokes in the middle cerebral artery (MCA) treated with intravenous tPA who were followed up during 3 months. Transcranial Doppler monitoring of recanalization was conducted during the first hours after tPA administration. The relative contribution of clinical, transcranial Doppler, and radiological information on stroke outcome was evaluated. We used logistic regression to derive a predictive model for good outcome (modified Rankin Scale score < or =2) after thrombolysis. RESULTS: Median National Institutes of Health Stroke Scale (NIHSS) score before tPA was 16. At 3 months, 87 patients (49.2%) became functionally independent (modified Rankin Scale score < or =2). In a logistic regression model, degree of recanalization within 300 minutes (P<0.001), proximal MCA occlusion (P<0.001), baseline NIHSS score (P=0.0013), systolic blood pressure (P=0.0116), and early ischemic changes on CT (P=0.0253) independently predicted outcome at 3 months. A 5-item score was developed on the basis of the factors significantly associated with stroke outcome in the logistic regression (total score range, 0 to 7). The likelihood of good outcome at 3 months was 0.82 (95% CI, 0.72 to 0.92) in patients who scored 0 to 2, 0.51 (95% CI, 0.36 to 0.66) in those who scored 3 to 4, and 0.15 (95% CI, 0.05 to 0.25) in those who scored 5 to 7 points. CONCLUSIONS: The combination of clinical, radiological, and hemodynamic information predicts with a high accuracy long-term stroke outcome during or shortly after intravenous tPA administration.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".