Importance of Early Ischemic Computed Tomography Changes Using ASPECTS in NINDS rtPA Stroke Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The importance of early ischemic change (EIC) on baseline computed tomography (CT) in the decision to thrombolyze the patient with acute ischemic stroke has been controversial. ASPECTS is a semiquantitative scale that scores the extent of EIC within the middle cerebral artery territory. We examined whether ASPECTS could be a treatment modifier by systematically reviewing the CT scans in the NINDS rtPA Stroke Study. METHODS: Six hundred eight of the 624 CT scans were available and of sufficient quality. One of 2 teams (n=3 each) of expert ASPECTS readers evaluated each scan for an ASPECTS value using a consensus score approach. Each team was blind to all clinical information except symptom side and blind to follow-up imaging and outcome information. ASPECTS values were stratified before analysis. Multivariable logistic regression was used to determine if an ASPECTS by treatment interaction existed on treatment response, outcome, and intracerebral hemorrhage risk. RESULTS: A total of 57.2% (348 of 608) of scans showed EIC with an ASPECTS <10. ASPECTS dichotomized into 8 to 10 and <8 did not have a treatment-modifying effect on good outcome but showed a trend to lower mortality at 90 days with tPA (relative risk 0.67, 95% confidence interval 0.41 to 1.06, P=0.10). ASPECTS 8 to 10 were associated with a trend to larger benefit of tPA with a number needed to treat (NNT) of 5 versus ASPECTS 3 to 7 with a NNT of 8. CONCLUSIONS: There was no evidence of treatment effect modification by the baseline ASPECTS value in the NINDS rtPA Stroke Study. Therefore, exclusion of patients for thrombolysis within 3 hours of symptom onset based on EIC is not supported by our data. There is a trend to reduced mortality and increased benefit to rtPA if the baseline CT scan is favorable (ASPECTS >7).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".