Abstract 108: Additional Value of Intracranial Vessel Wall Imaging to Determine the Etiology of Ischemic Stroke
Bibliographic record
Abstract
Objective: High-resolution MR-imaging of the intracranial arterial wall is a promising technique for diagnosis of intracranial arteriopathies in patients with ischemic stroke. We aimed to evaluate the additional value of vessel wall imaging (VWI) to the standard work-up of ischemic stroke patients. Methods: We selected all patients with ischemic stroke who had intracranial VWI at our institute to evaluate possible intracranial arteriopathy, such as atherosclerosis, dissection, vasculitis, or reversible cerebral vasoconstriction syndrome. Two observers, who were blinded to the VWI, first determined the most likely stroke etiology based on the standard work-up (clinical history, brain parenchyma imaging, vessel lumen imaging, laboratory results, and cardiac work-up). Then VWI was reviewed to assess whether this would change the suspected stroke etiology or whether the differential diagnosis could be narrowed down. Results: Between 2006 and 2014, 199 patients with ischemic stroke, mean age 55 (IQ-range: 44-67) had VWI. VWI provided additional information to the standard stroke work-up in 128 patients (64%). In 38/199 patients (19%) the conclusion on stroke etiology was altered based on VWI and in 90/199 patients (45%) the differential diagnosis was further narrowed after VWI. VWI did not have additional value when the most likely stroke etiology based on the standard work-up remained the same (50/199 patients; 25%), when the differential diagnosis could not be narrowed down (16/199; 8%), or in case of poor image quality (5/199 patients; 3%). Patients under the age of 46 benefited more often from VWI than older adults (Odds Ratio 3.5; 95%CI: 1.7-7.6). Conclusion: VWI provided additional information to the conventional stroke work-up in almost two-thirds of patients suspected to have intracranial arteriopathy. Next step is to determine how frequently this additional information resulted in altered therapy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".