“Clinical-CT Mismatch” and the Response to Systemic Thrombolytic Therapy in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Mismatch between clinical deficits and imaging lesions in acute stroke has been proposed as a method of identifying patients who have hypoperfused but still have viable brain, and may be especially apt to respond to reperfusion therapy. We explored this hypothesis using a combined database including 4 major clinical trials of intravenous (IV) thrombolytic therapy. METHODS: To determine what the radiological correlates of a "matched" functional deficit are, we calculated the relationship between the ASPECT score of the 24-hour (follow-up) CT scan and the 24-hour National Institutes of Health Stroke Scale (NIHSS) score on the subsample with ASPECT scores performed at this time (n=820). Based on this empirical relationship, we computed the absolute difference between the observed baseline ASPECT score and the "expected" score (ie, matched) based on baseline NIHSS for all patients (n=2131). We tested whether patients with better than expected baseline ASPECTS were more likely to benefit from IV recombinant tissue plasminogen activation (rtPA). RESULTS: At 24 hours, there was a strong, linear, negative correlation between NIHSS and ASPECTS (r2=0.33, P<0.0001); on average, an increase of 10 points on NIHSS corresponded to a decrease of approximately 3 points on ASPECTS. At baseline, the average degree of mismatch between the observed and "expected" ASPECTS was 2.1 points (interquartile range, 1.0 to 3.4). However, multiple analyses failed to reveal a consistent relationship between the degree of clinical-CT mismatch at baseline and a patient's likelihood of benefiting from IV rtPA. CONCLUSIONS: Clinical-CT mismatch using ASPECT scoring does not reliably identify patients more or less likely to benefit from IV rtPA.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".