Normal Magnetic Resonance Perfusion-Weighted Imaging in Lacunar Infarcts Predicts a Low Risk of Early Deterioration
Bibliographic record
Abstract
BACKGROUND: Current clinical tools to identify lacunar infarct patients at risk of deterioration are inadequate, and imaging techniques to predict fluctuation and deterioration would be of value. We sought to determine the occurrence of MRI perfusion-weighted imaging (PWI) abnormalities in lacunes, and whether they help predict clinical and radiological outcome. METHODS: Patients with lacunar stroke or TIA were selected from a prospective MR imaging study. MRI was performed within 24 h of the event and follow-up imaging completed at 30 or 90 days. Baseline perfusion maps were qualitatively assessed and infarct volumes measured. Early clinical deterioration (NIHSS worsening of > or = 3 points within 72 h of event) and 90-day modified Rankin Scale score (mRS) were recorded. RESULTS: Twenty-two patients were included. Fifteen (68.2%) had abnormal PWI at the site of the diffusion-weighted imaging lesion. Patients with abnormal PWI were more likely to have stroke than TIA as their index event (RR 2.2, 95% CI 0.9-5.2, p = 0.02). Early clinical deterioration occurred in 4 patients (18.2%), all of whom had abnormal PWI. PWI lesions were not associated with a higher 90-day NIHSS or mRS score, nor did they predict infarct volume growth. CONCLUSIONS: MR-PWI abnormalities are seen in two thirds of lacunar infarcts, and are associated with stroke rather than TIA. Normal PWI identifies patients at low risk of early clinical deterioration.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".