Interaction between time, ASPECTS, and clinical mismatch
Bibliographic record
Abstract
BACKGROUND: Imaging-based patient selection for neurothrombectomy is reliant on the identification of irreversibly damaged brain tissue (core) and salvageable tissue (penumbra). The DAWN trial used the clinical-core mismatch (CCM) paradigm (clinical deficit out of proportion to infarct volume). We aim to determine the prevalence of CCM in large vessel occlusion (LVO) strokes and study the impact of time and the Alberta Stroke Program Early CT Score (ASPECTS) on the likelihood of mismatch. METHODS: We performed a retrospective observational analysis of internal carotid artery/middle cerebral artery M1 occlusions with available advanced imaging (relative cerebral blood flow/MRI). We used automated software for infarct volume analysis and ASPECTS determination. The prevalence of CCM and the impact of time and ASPECTS were analyzed. RESULT: One hundred and eighty-five LVO strokes were included. Mean age was 71±15 years and median National Institutes of Health Stroke Scale score was 17 (range 12-21). Mean ischemic core volume was 50±69 mL. Within 0-24 hours, CCM was present in 53% and ranged from 63% in 0-3 hours to 25% at 21-24 hours (p=0.03). Prevalence of mismatch reduced 1.6% for every 1 hour increase in time to imaging. CCM prevalence by ASPECTS groups was: ASPECTS 9-10: 77%, ASPECTS 6-8: 65%, ASPECTS 0-5: 13% (p<0.01), with a 6.4% decrement for every 1 point decrease in ASPECTS. The prevalence of mismatch did not diminish over time among ASPECTS groups and higher ASPECTS was an independent predictor of CCM (OR 1.4 (95% CI 1.1 to 1.7), p<0.001). CONCLUSIONS: CCM is present in 57% and 50% of LVO strokes in the 0-6 and 6-24 hour window, respectively. The prevalence of mismatch declines with increasing time (1.6%/hour) and decreasing ASPECTS (6.4%/point). Among ASPECTS groups the prevalence of mismatch does not decline over time. These data support the use of an ASPECTS-based paradigm for late window patient selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".