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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".