Impact of Initial Imaging Protocol on Likelihood of Endovascular Stroke Therapy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Noncontrast head CT and CT perfusion (CTP) are both used to screen for endovascular stroke therapy (EST), but the impact of imaging strategy on likelihood of EST is undetermined. Here, we examine the influence of CTP utilization on likelihood of EST in patients with large vessel occlusion (LVO). METHODS: We identified patients with acute ischemic stroke at 4 comprehensive stroke centers. All 4 hospitals had 24/7 CTP and EST capability and were covered by a single physician group (Neurology, NeuroIntervention, NeuroICU). All centers performed noncontrast head CT and CT angiography in the initial evaluation. One center also performed CTP routinely with high CTP utilization (CTP-H), and the others performed CTP optionally with lower utilization (CTP-L). Primary outcome was likelihood of EST. Multivariable logistic regression was used to determine whether facility type (CTP-H versus CTP-L) was associated with EST adjusting for age, prestroke mRS, National Institutes of Health Stroke Scale, Alberta Stroke Program Early CT Score, LVO location, time window, and intravenous tPA (tissue-type plasminogen activator). RESULTS: <0.01). In univariable analysis, EST rates for patients with LVO were similar between CTP-H versus CTP-L (46% versus 49%). In multivariable analysis, patients with LVO were less likely to undergo EST at CTP-H (odds ratio, 0.59 [0.41-0.85]). This finding was maintained in multiple patient subsets including late time window, anterior circulation LVO, and direct presentation patients. Ninety-day functional independence (odds ratio, 1.04 [0.70-1.54]) was not different, nor were rates of post-EST PH-2 hemorrhage (1% versus 1%). CONCLUSIONS: We identified an increased likelihood for undergoing EST in centers with lower CTP utilization, which was not associated with worse clinical outcomes or increased hemorrhage. These findings suggest under-treatment bias with routine CTP.
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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 | 0.022 |
| 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) | 0.003 | 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".