Using the baseline CT scan to select acute stroke patients for IV-IA therapy.
Bibliographic record
Abstract
BACKGROUND: Intra-arterial therapies for acute ischemic stroke are increasingly available. Intravenous therapy (IV) followed immediately by intra-arterial therapy (IA) has been shown to be safe, but such therapy is resource intensive. Selecting the best patients for this therapy may be accomplished with the use of baseline neuroimaging. METHODS: We used data from the IMS-1 and National Institute for Neurological Disorders and Stroke tissue plasminogen activator (tPA) stroke studies to compare outcomes among IV-IA tPA, IV-tPA, and placebo treatment stratified by the baseline CT scan appearance. The CT scans were scored using the Alberta Stroke Program Early CT (ASPECT) score and dichotomized into ASPECT score > 7 (favorable scan) and ASPECT score < or = 7 (unfavorable scan). Logistic regression was used to assess for an ASPECT score by treatment interaction. RESULTS: A total of 460 patients was included. Age and sex were similar among the 3 groups. The IV-IA tPA cohort had a higher median National Institutes of Health stroke scale (NIHSS) score (18 versus 17) compared with the IV tPA cohort. The proportion of patients with favorable CT scans (ASPECT score > 7) was lowest in the IV-IA tPA group. A multiplicative interaction effect was shown indicating that patients with an ASPECT score > 7 in the IV-IA cohort were more likely to have a good outcome compared with IV tPA and with placebo. Harm may accrue to patients treated with IV-IA therapy who have an unfavorable baseline CT scan appearance. CONCLUSIONS: Patients with a favorable baseline CT scan appearance are the most likely to benefit from IV-IA therapy. This hypothesis will be tested in the IMS-3 study.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".