MT-DRAGON score for outcome prediction in acute ischemic stroke treated by mechanical thrombectomy within 8 hours
Bibliographic record
Abstract
OBJECTIVES: The MRI-DRAGON score includes clinical and MRI parameters and demonstrates a high specificity in predicting 3 month outcome in patients with acute ischemic stroke (AIS) treated with intravenous tissue plasminogen activator (IV tPA). The aim of this study was to adapt this score to mechanical thrombectomy (MT) in a large multicenter cohort. METHODS: Consecutive cases of AIS treated by MT between January 2015 and December 2017 from three stroke centers were reviewed (n=1077). We derived the MT-DRAGON score by keeping all variables of the MRI-DRAGON score (age, initial National Institutes of Health Stroke Scale score, glucose level, pre-stroke modified Rankin Scale (mRS) score, diffusion weighted imaging-Alberta Stroke Program Early CT score ≤5) and considering the following variables: time to groin puncture instead of onset to IV tPA time and occlusion site. Unfavorable 3 month outcome was defined as a mRS score >2. Score performance was evaluated by c statistics and an external validation was performed. RESULTS: Among 679 included patients (derivation and validation cohorts, n=431 and 248, respectively), an unfavorable outcome was similar between the derivation (51.5%) and validation (58.1%, P=0.7) cohorts, and was significantly associated with all MT-DRAGON parameters in the multivariable analysis. The c statistics for unfavorable outcome prediction was 0.83 (95%CI 0.79 to 0.88) in the derivation and 0.8 (95%CI 0.75 to 0.86) in the validation cohort. All patients (n=55) with an MT-DRAGONscore ≥11 had an unfavorable outcome and 60/63 (95%) patients with an MT-DRAGON score ≤2 points had a favorable outcome. CONCLUSION: The MT-DRAGON score is a simple tool, combining admission clinical and radiological parameters that can reliably predict 3 month outcome after MT.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".