Predicting Imaging Outcomes in Acute Stroke Therapy—Comparison of Magnetic Resonance Imaging and Computed Tomography
Bibliographic record
Abstract
Abstract Background Imaging of acute stroke patients in emergency settings is critical for treatment decisions. Most commonly, CT with CTA is used worldwide for acute stroke. However, MRI may be advantageous in certain settings. With advancements in endovascular clot retrieval techniques, there is a need to identify and use the best possible imaging for the diagnosis and outcome prediction of hyperacute stroke. Methods This mixed retrospective and prospective observational study was conducted over 2 years in patients who underwent reperfusion therapies. Patients were included in this study if they had a baseline as well as follow-up noncontrast CT and diffusion-weighted imaging (DWI) MRI. We compared them for estimating final infarct size and outcomes after reperfusion therapy. Results A total of 86 patients were included in the study. Baseline DWI found new infarcts in 33 patients compared to baseline CT. Sensitivity and specificity of CT and DWI in predicting the final infarct size was 75.3% and 76.9% and 97.2% and 92.3%, respectively. A positive correlation of 51.2% and 84.4% was noted between b-CT Alberta stroke programme early CT score (ASPECTS) and b-DWI with 72 hours DWI ASPECTS, respectively (p < 0.001). The positive predictive value of CT was 94.8% and DWI was 98.6%. None of the patients had reversible hyperintensities in the follow-up DWI. Conclusion MRI is more sensitive and specific than noncontrast CT in predicting final infarct volume. It predicts final outcomes better and could be an alternative if available in acute stroke settings.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".