Reliability of Assessing Percentage of Diffusion-Perfusion Mismatch
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Emergent neurovascular imaging holds promise in identifying new and optimum target populations for thrombolysis in stroke. Recent research has focused on patients with diffusion-weighted MRI (DWI)-perfusion-weighted MRI (PWI) mismatch as a marker of tissue at risk of infarction and a means to select the most suitable candidates for thrombolysis. The present study sought to estimate the reliability of assessing the percentage of DWI-PWI mismatch. METHODS: Thirteen patients with acute strokes had DWI and PWI within 7 hours of symptom onset. Six raters independently created relative mean transit time (rMTT) maps and then compared them with DWI images to assess the percentage of mismatch (PWI>DWI) in 10% increments. The MR scans were reassessed by 4 raters, tracing around the lesions to calculate the volume percentage of mismatch. RESULTS: Visual assessment had an interrater reliability of 0.68 (95% CI, 0.52 to 1.0; SEM=21.6%) and an intrarater reliability of 0.80 (95% CI, 0.47 to 1.0; SEM=16.9%). Hand-drawn assessment had an interrater reliability of 0.66 (95% CI, 0.45 to 1.0; SEM=26.2%) and an intrarater reliability of 0.94 (95% CI, 0.81 to 1.0; SEM=10.9%). CONCLUSIONS: Results from the present study suggest that quantifying mismatch by the human eye is reproducible but not reliable among observers. This raises doubts about using mismatch for clinical decision making and clinical trial enrollment.
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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 | 0.027 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".