Magnetic resonance imaging profiles predict clinical response to early reperfusion: The diffusion and perfusion imaging evaluation for understanding stroke evolution (DEFUSE) study
Bibliographic record
Abstract
OBJECTIVE: To determine whether prespecified baseline magnetic resonance imaging (MRI) profiles can identify stroke patients who have a robust clinical response after early reperfusion when treated 3 to 6 hours after symptom onset. METHODS: We conducted a prospective, multicenter study of 74 consecutive stroke patients admitted to academic stroke centers in North America and Europe. An MRI scan was obtained immediately before and 3 to 6 hours after treatment with intravenous tissue plasminogen activator 3 to 6 hours after symptom onset. Baseline MRI profiles were used to categorize patients into subgroups, and clinical responses were compared based on whether early reperfusion was achieved. RESULTS: Early reperfusion was associated with significantly increased odds of achieving a favorable clinical response in patients with a perfusion/diffusion mismatch (odds ratio, 5.4; p = 0.039) and an even more favorable response in patients with the Target Mismatch profile (odds ratio, 8.7; p = 0.011). Patients with the No Mismatch profile did not appear to benefit from early reperfusion. Early reperfusion was associated with fatal intracranial hemorrhage in patients with the Malignant profile. INTERPRETATION: For stroke patients treated 3 to 6 hours after onset, baseline MRI findings can identify subgroups that are likely to benefit from reperfusion therapies and can potentially identify subgroups that are unlikely to benefit or may be harmed.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".