Abstract T P292: Baseline Infarct Volume Predicts Disability in TIA and Minor Stroke Patients
Bibliographic record
Abstract
BACKGROUND: Despite their mild presenting neurological deficit, over one third of patients with transient ischemic attack (TIA) and minor stroke are dead or disabled at the time of hospital discharge. This is predominantly related to either symptom progression or recurrent stroke, although predicting outcome can be difficult. We sought to determine whether baseline radiographic MR characteristics could predict disability at 3months in this population. METHODS: Consecutive TIA/minor stroke (National Institutes of Health Stroke Scale<4) that were not disabled at baseline and had an MRI within 24 hours of symptom onset were prospectively included. Disability was assessed at 90 days using the modified Rankin Scale (mRS). The impact of perfusion (PWI) and diffusion (DWI) variables on disability (mRS≥2) at 90 days was assessed. RESULTS: 418 patients were included; 55.5% had positive DWI lesions. 292 patients had PWI imaging of whom 35% had PWI deficit (Tmax≥2s) and 26.5% had mismatch (Tmax≥4s-DWI) at baseline. The median DWI, PWI and mismatch volumes were 1.14 ml (IQR=3.43), 9.8 ml (IQR=29.8) and 9 ml respectively. A total of 56/418 (13.4%) patients were disabled at 90days. In multivariable analysis we adjusted for baseline predictors of disability (age, DM, premorbid mRS 1, ongoing symptoms, baseline NIHSS, CT/CT angiography-positive metric and DWI or PWI volume). DWI volume (OR=1.05, p=0.007), and age (OR=1.03, p=0.003) remained independent predictors of disability. PWI or mismatch volume did not predict functional outcome. CONCLUSIONS: A substantial proportion of patients with TIA and minor stroke are disabled at 90days. The degree of tissue injury as measured by DWI volume is an independent predictor of disability regardless of the mechanism of disability.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".