Magnetic Resonance Imaging-DRAGON Score
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The DRAGON score, which includes clinical and computed tomographic scan parameters, showed a high specificity to predict 3-month outcome in patients with acute ischemic stroke treated by intravenous tissue plasminogen activator. We adapted the score for patients undergoing MRI as the first-line diagnostic tool. METHODS: We reviewed patients with consecutive anterior circulation ischemic stroke treated ≤ 4.5 hour by intravenous tissue plasminogen activator between 2003 and 2012 in our center, where MRI is systematically implemented as first-line diagnostic work-up. We derived the MRI-DRAGON score keeping all clinical parameters of computed tomography-DRAGON (age, initial National Institutes of Health Stroke Scale and glucose level, prestroke handicap, onset to treatment time), and considering the following radiological variables: proximal middle cerebral artery occlusion on MR angiography instead of hyperdense middle cerebral artery sign, and diffusion-weighted imaging Alberta Stroke Program Early Computed Tomography Score (DWI ASPECTS) ≤ 5 instead of early infarct signs on computed tomography. Poor 3-month outcome was defined as modified Rankin scale >2. We calculated c-statistics as a measure of predictive ability and performed an internal cross-validation. RESULTS: Two hundred twenty-eight patients were included. Poor outcome was observed in 98 (43%) patients and was significantly associated with all parameters of the MRI-DRAGON score in multivariate analysis, except for onset to treatment time (nonsignificant trend). The c-statistic was 0.83 (95% confidence interval, 0.78-0.88) for poor outcome prediction. All patients with a MRI-DRAGON score ≤ 2 (n=22) had a good outcome, whereas all patients with a score ≥ 8 (n=11) had a poor outcome. CONCLUSIONS: The MRI-DRAGON score is a simple tool to predict 3-month outcome in acute stroke patients screened by MRI then treated by intravenous tissue plasminogen activator and may help for therapeutic decision.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".