Abstract 116: MR PREDICTS@24H -- Multivariable Outcome Prediction After Endovascular Treatment for Acute Ischemic Stroke: Development and Validation of a Prognostic Model in Data From Seven RCTs
Bibliographic record
Abstract
Background: Even when the revascularization and clinical status of a patient after endovascular treatment (EVT) for acute ischemic stroke is known, outcome is still highly variable and difficult to predict. We aimed to develop and externally validate a prognostic model that can be applied within one day after EVT to predict functional outcome at three months (MR PREDICTS@24H). Methods: For model development we used data from patients in the treatment arms of seven randomized controlled trials within the HERMES collaboration. For external validation we used data from the MR CLEAN Registry, a Dutch ongoing prospective multicenter study for consecutive patients treated with EVT between March 2014 and June 2016 (n=1526). Primary outcome was the ordinal modified Rankin Scale (mRS) score three months after EVT. Eighteen pre- and post-procedural variables, assessed within one day after EVT, were analyzed with univariable ordinal logistic regression analysis (p<0.157) and multivariable ordinal logistic regression analysis with stepwise backward selection (p Results: The final model included nine variables: age, baseline stroke severity measured with the NIH Stroke Scale (NIHSS), diabetes mellitus, pre-stroke mRS, collateral score, occlusion location, revascularization grade, NIHSS 24 hours after EVT, and symptomatic intracranial hemorrhage. The model explained 62% of the variance in outcome and NIHSS 24 hours after EVT was the strongest predictor with 54% explained variance. The externally validated c-statistic was 0.84 for the prediction of the ordinal mRS and 0.91 for mRS 0-2, indicating very good model performance. Calibration for mRS 0-2 was also very good (intercept: 0.25 and slope: 0.99). Conclusion: MR PREDICTS@24H, which can be applied within one day after EVT, accurately predicts functional outcome at three months. It may provide physicians, patients, and family members with improved outcome expectations and could guide physicians in personalizing their patients’ treatment and rehabilitation plans.
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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 | 0.133 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".