Impact of early MRI in ischemic strokes beyond hyper-acute stage to improve patient outcomes, enable early discharge, and realize cost savings
Bibliographic record
Abstract
BACKGROUND: Early in-patient MR Imaging may assist in identifying stroke etiology, facilitating prompt secondary prevention for ischemic strokes (IS), and potentially enhancing patient outcomes. This study explores the impact of early in patient MRI on IS patient outcomes and healthcare resource use beyond the hyper-acute stage. METHODS: In this retrospective registry-based study, 771 admitted transient ischemic attack (TIA) and IS patients at Halifax's QEII Health Centre from 2015 to 2019 underwent in-patient MRI. Cohort was categorized into two groups based on MRI timing: early (within 48 h) and late. Logistic regression and Poisson log-linear models, adjusted for age, sex, stroke severity, acute stroke protocol (ASP) activation, thrombolytic, and thrombectomy, were employed to examine in-hospital, discharge, post-discharge, and healthcare resource utilization outcomes. RESULTS: Among the cohort, 39.6 % received early in-patient MRI. ASP activation and TIA were associated with a higher likelihood of receiving early MRI. Early MRI was independently associated with a lower rate of symptomatic changes in neurological status during hospitalization (adjusted odds ratio [OR], 0.42; 95 % confidence interval [CI], 0.20-0.88), higher odds of good functional outcomes at discharge (1.55; 1.11-2.16), lower rate of non-home discharge (0.65; 0.46-0.91), shorter length of stay (regression coefficient, 0.93; 95 % CI, 0.89-0.97), and reduced direct cost of hospitalization (0.77; 0.75-0.79). CONCLUSION: Early in-patient MRI utilization in IS patients post-hyper-acute stage was independently associated with improved patient outcomes and decreased healthcare resource utilization, underscoring the potential benefits of early MRI during in-patient management of IS. Further research, including randomized controlled trials, is warranted to validate these findings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".