The iScore Predicts Poor Functional Outcomes Early After Hospitalization for an Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The iScore is a prediction tool originally developed to estimate the risk of death after hospitalization for an acute ischemic stroke. Our objective was to determine whether the iScore could also predict poor functional outcomes. METHODS: We applied the iScore to patients presenting with an acute ischemic stroke at multiple hospitals in Ontario, Canada, between 2003 and 2008, who had been identified from the Registry of the Canadian Stroke Network regional stroke center database (n=3818) and from an external data set, the Registry of the Canadian Stroke Network Ontario Stroke Audit (n=4635). Patients were excluded if they were included in the sample used to develop and validate the initial iScore. Poor functional outcomes were defined as: (1) death at 30 days or disability at discharge, in which disability was defined as having a modified Rankin Scale 3 to 5; and (2) death at 30 days or institutionalization at discharge. RESULTS: The prevalence of poor functional outcomes in the Registry of the Canadian Stroke Network and the Ontario Stroke Audit, respectively, were 55.7% and 44.1% for death at 30 days or disability at discharge and 16.9% and 16.2%, respectively, for death at 30 days or institutionalization at discharge. The iScore stratified the risk of poor outcomes in low- and high-risk individuals. Observed versus predicted outcomes showed high correlations: 0.988 and 0.940 for mortality or disability and 0.985 and 0.993 for mortality or institutionalization in the Registry of the Canadian Stroke Network and Ontario Stroke Audit cohorts. CONCLUSIONS: The iScore can be used to estimate the risk of death or a poor functional outcome after an acute ischemic stroke.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".