Predictors of poor outcomes in First-Event Ischemic Stroke as assessed by Magnetic Resonance Imaging
Bibliographic record
Abstract
PURPOSE: Stroke is the third most common cause of mortality worldwide and is a major cause of permanent disability. The purposed of the study was to better understand the risk factors for poor outcomes following ischemic stroke requiring treatment. METHODS: Three hundred seventy patients with first-event ischemic stroke were enrolled. Good outcomes was defined as a using the Modified Rankin Scale (MRS) score ≤3 without any cardiovascular event, while poor outcomes were any of the following end points: MRS >3 at 3 months, recurrent stroke or death. Prognostic variables for poor outcomes were analyzed based on a stepwise logistic regression model. RESULTS: Seventy-eight patients had poor outcomes (21%, 78/370), assessed at a minimum of six-month follow-up. Higher mean National Institutes of Health Stroke Scale (NIHSS) scores at presentation, presence of early neurologic deterioration (END) and higher mean high-sensitivity C-reactive protein (hs-CRP) levels were associated with poor outcomes at discharge. Furthermore, both NIHSS at presentation and the presence of END were associated with poor outcomes, assessed at a minimum of six-month follow-up. CONCLUSION: A higher mean initial NIHSS score implies not only severe neurologic deficits but also an increased risk of poor outcomes. Since END following ischemic stroke is frequently associated with poor outcomes, more attention should be directed to providing adequate treatment to patients in the acute stage, especially for high risk patients.
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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 | 0.000 | 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 | 0.001 |
| 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".