Recovery after Ischemic Stroke: Criteria for Good Outcome by Level of Disability at Day 7
Bibliographic record
Abstract
BACKGROUND: Ischemic stroke is a leading cause of morbidity. Assessing the chances of recovery is critical to optimize poststroke care. METHODS: We used a cohort of patients from the Virtual International Stroke Trial Archive that participated in acute stroke trials (control arm) and were followed for 90 days. The cohort was grouped by day 7 (D7) modified Rankin scale (mRS) scores. Variables that were associated with good outcome (mRS 0-2 at 90 days) in the univariate analysis were entered into a logistic regression model to determine the independent good outcome criteria for each D7 mRS tier. RESULTS: We analyzed 1,798 patients. The independent good outcome criteria identified for different mRS tiers were: D7 mRS of 3: age < or =70, 0-2 vascular risk factors, D7 NIH Stroke Scale (NIHSS) arm strength < or =1, D7 NIHSS language score = 0; D7 mRS of 4: age < or =70, male, D7 NIHSS facial palsy < or =1, D7 NIHSS visual = 0, D7 NIHSS leg strength < or =1, D7 NIHSS dysarthria = 0; D7 mRS of 5: age < or =70, IV tPA treatment, D7 NIHSS dysarthria = 0, D7 NIHSS leg strength < or =2. For each mRS tier, we observed a graded increase in the percentage of the primary and secondary end points with increase in the number of criteria. CONCLUSIONS: We identified clinical variables that predict good outcome, are specific to each day 7 mRS tier, and enable easy and informative assessment of the patient's likelihood of achieving varying degrees of recovery at day 90. These results may be useful in both clinical practice and research but require validation in an independent patient cohort.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".