Acute ischemic lesions of varying ages predict risk of ischemic events in stroke/TIA patients
Bibliographic record
Abstract
BACKGROUND: Multiple ischemic lesions identified by diffusion-weighted imaging (DWI) have been shown to predict high risk of future ischemic events. However, the importance of lesion age has not been factored into this risk. Our goal was to evaluate whether the presence of ischemic lesions of varying ages identified by DWI and apparent diffusion coefficient (ADC) suggests a higher risk of future ischemic events. METHODS: Patients with acute stroke and TIA presenting within 12 hours of symptom onset who had a baseline and 1-month follow-up MRI were enrolled in the study. Acute ischemic lesions were divided into DWI positive with ADC low lesions and DWI positive with ADC normalized lesions. The baseline MRI and the presence of new lesions on the follow-up MRI were analyzed. RESULTS: A total of 360 patients were prospectively enrolled, and all had appropriate imaging. Two hundred twenty-three were excluded as there were no DWI lesions, they received recombinant tissue plasminogen activator, or they did not have the 30-day follow-up MRI. One hundred seventeen patients had DWI lesions of one age (DWI positive with either ADC low lesions or ADC normalized lesions alone) and 20 had lesions of varying ages (DWI positive lesions with reduced and normalized ADC) on the baseline MRI. Patients with multiple DWI lesions of varying ages were at more risk of having new lesions on the 30-day MRI compared with those having lesions of the same age (relative risk = 3.6; 95% CI 1.9 to 6.8). Multiple DWI lesions of varying ages (odds ratio [OR] 6.6; 95% CI 2.3 to 19.1) and cardioembolic stroke subtype (OR 3.2; 95% CI 1.1 to 8.7) were independently associated with new lesion recurrence by multiple logistic regression analysis. CONCLUSION: The presence of multiple diffusion-weighted imaging lesions of varying ages suggests very active early recurrence over time and portends a higher early risk of future ischemic events.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".