Value of Computed Tomographic Perfusion–Based Patient Selection for Intra-Arterial Acute Ischemic Stroke Treatment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The utility of computed tomographic perfusion (CTP)-based patient selection for intra-arterial treatment of acute ischemic stroke has not been proven in randomized trials and requires further study in a cohort that was not selected based on CTP. Our objective was to study the relationship between CTP-derived parameters and outcome and treatment effect in patients with acute ischemic stroke because of a proximal intracranial arterial occlusion. METHODS: We included 175 patients who underwent CTP in the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in The Netherlands (MR CLEAN). Association of CTP-derived parameters (ischemic-core volume, penumbra volume, and percentage ischemic core) with outcome was estimated with multivariable ordinal logistic regression as an adjusted odds ratio for a shift in the direction of a better outcome on the modified Rankin Scale. Interaction between CTP-derived parameters and treatment effect was determined using multivariable ordinal logistic regression. Interaction with treatment effect was also tested for mismatch (core <70 mL; penumbra core >1.2; penumbra core >10 mL). RESULTS: The adjusted odds ratio for improved functional outcome for ischemic core, percentage ischemic core, and penumbra were 0.79 per 10 mL (95% confidence interval: 0.71-0.89; P<0.001), 0.82 per 10% (95% confidence interval: 0.66-0.90; P=0.002), and 0.97 per 10 mL (96% confidence interval: 0.92-1.01; P=0.15), respectively. No significant interaction between any of the CTP-derived parameters and treatment effect was observed. We observed no significant interaction between mismatch and treatment effect. CONCLUSIONS: CTP seems useful for predicting functional outcome, but cannot reliably identify patients who will not benefit from intra-arterial therapy.
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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 | 0.000 | 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".