“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 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".