Visual Assessment of Perfusion-Diffusion Mismatch Is Inadequate to Select Patients for Thrombolysis
Bibliographic record
Abstract
BACKGROUND: For MR perfusion-diffusion mismatch to be clinically useful as a means of selecting patients for thrombolysis, it needs to occur in real time at the MRI console. Visual mismatch assessment has been used clinically and in trials but has not been systematically validated. We compared the accuracy of visually rating console-generated images with offline volumetric measurements using data from the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET). METHODS: Perfusion time-to-peak (TTP) and diffusion-weighted images (DWI) (as generated by commercial MRI console software) and T(max) perfusion maps (which required offline calculation) were visually rated. Perfusion-diffusion mismatch, defined as a ratio of perfusion:diffusion lesion volume of >1.2, was independently scored by 1 expert and 2 inexperienced raters blinded to calculated volumes and clinical information. Visual mismatch was compared with region-of-interest-based volumetric calculation, which was used as the gold standard. RESULTS: Volumetric calculation demonstrated perfusion-diffusion mismatch in 85/99 patients. Visual TTP-DWI mismatch was correctly classified by the experienced rater in 82% of the cases (sensitivity: 0.86; specificity: 0.54) compared to 73% for the inexperienced raters (sensitivity: 0.75; specificity: 0.57). The interrater reliability for TTP-DWI mismatch was moderate (kappa = 0.50). Visual T(max)-DWI mismatch performed better (agreement - 93 and 87%, sensitivity - 95 and 88%, specificity - 77 and 82% for the experienced and inexperienced raters, respectively). CONCLUSIONS: The assessment of visual TTP-DWI mismatch at the MRI console is insufficiently reliable for use in clinical trials. Differences in perfusion analysis technique and visual inaccuracies combine to make visual TTP-DWI mismatch substantially different to volumetric T(max)-DWI mismatch. Automated software that applies perfusion thresholds may improve the reproducibility of real-time mismatch assessment.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".