Addition of Hyperacute MRI Aids in Patient Selection, Decreasing the Use of Endovascular Stroke Therapy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The failure of recent trials to show the effectiveness of acute endovascular stroke therapy (EST) may be because of inadequate patient selection. We implemented a protocol to perform pretreatment MRI on patients with large-vessel occlusion eligible for EST to aid in patient selection. METHODS: We retrospectively identified patients with large-vessel occlusion considered for EST from January 2008 to August 2012. Patients before April 30, 2010, were selected based on computed tomography/computed tomography angiography (prehyperacute protocol), whereas patients on or after April 30, 2010, were selected based on computed tomography/computed tomography angiography and MRI (hyperacute MRI protocol). Demographic, clinical features, and outcomes were collected. Univariate and multivariate analyses were performed. RESULTS: We identified 267 patients: 88 patients in prehyperacute MRI period and 179 in hyperacute MRI period. Fewer patients evaluated in the hyperacute MRI period received EST (85 of 88, 96.6% versus 92 of 179, 51.7%; P<0.05). The hyperacute-MRI group had a more favorable outcome of a modified Rankin scale 0 to 2 at 30 days as a group (6 of 66, 9.1% versus 33 of 140, 23.6%; P=0.01), and when taken for EST (6 of 63, 9.5% versus 17 of 71, 23.9%; P=0.03). On adjusted multivariate analysis, the EST in the hyperacute MRI period was associated with a more favorable outcome (odds ratio, 3.4; 95% confidence interval, 1.1-10.6; P=0.03) and reduced mortality rate (odds ratio, 0.16; 95% confidence interval, 0.03-0.37; P<0.001). CONCLUSIONS: Implementation of hyperacute MRI protocol decreases the number of endovascular stroke interventions by half. Further investigation of MRI use for patient selection is warranted.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".