Rapid Assessment of Perfusion–Diffusion Mismatch
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: For MR perfusion-diffusion (PWI-DWI) mismatch to become routine in thrombolysis patient selection, rapid and reliable assessment tools are required. We examined interrater variability in PWI/DWI volume measurements and developed a rapid assessment tool based on the Alberta Stroke Program Early CT Scores (ASPECTS) system. METHODS: DWI and PWI were performed in 35 patients with stroke <6 hours after symptom onset. DWI lesion and PWI (time to peak) volumes were measured with planimetric techniques by 4 raters and the 95% limits of agreement calculated. ASPECT scores were assessed separately by 4 investigators (2 experienced and 2 inexperienced) for DWI (MR DWI scores) and PWI (MR time to peak scores). MR mismatch scores were calculated as MR DWI-MR time to peak scores. RESULTS: Interobserver variability was much greater for PWI (95% limit of agreement=+/-72.3 mL) than for DWI (95% limit of agreement=+/-12.6 mL). A semiautomated PWI volume (time to peak+2 s) was therefore used to calculate mismatch volume. MR mismatch scores >or=2 predicted 20% PWI-DWI mismatch by volume with mean 78% sensitivity (range, 72% to 84%) and 88% specificity (range, 83% to 90%). There was excellent agreement on mismatch classification using MR mismatch scores between experienced raters (weighted kappa scores of 0.94) with agreement in 34 of 35 cases. Agreement was less consistent between inexperienced raters (weighted kappa=0.49, 28 of 35 cases). CONCLUSIONS: Variability in planimetric mismatch measurements arises primarily from differences in PWI volume assessment. High specificity and interrater reliability may make MR mismatch scores an ideal rapid screening tool for potential thrombolysis patients.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".