International Survey of Acute Stroke Imaging Used to Make Revascularization Treatment Decisions
Bibliographic record
Abstract
BACKGROUND: To assess the differences across continental regions in terms of stroke imaging obtained for making acute revascularization therapy decisions, and to identify obstacles to participating in randomized trials involving multimodal imaging. METHODS: STroke Imaging Repository (STIR) and Virtual International Stroke Trials Archive (VISTA)-Imaging circulated an online survey through its website, through the websites of national professional societies from multiple countries as well as through email distribution lists from STIR and the above mentioned societies. RESULTS: We received responses from 223 centers (2 from Africa, 38 from Asia, 10 from Australia, 101 from Europe, 4 from Middle East, 55 from North America, 13 from South America). In combination, the sites surveyed administered acute revascularization therapy to a total of 25,326 acute stroke patients in 2012. Seventy-three percent of these patients received intravenous (i.v.) tissue plasminogen activator (tPA), and 27%, endovascular therapy. Vascular imaging was routinely obtained in 79% (152/193) of sites for endovascular therapy decisions, and also as part of standard IV tPA treatment decisions at 46% (92/198) of sites. Modality, availability and use of acute vascular and perfusion imaging before revascularization varied substantially between geographical areas. The main obstacles to participate in randomized trials involving multimodal imaging included: mainly insufficient research support and staff (50%, 79/158) and infrequent use of multimodal imaging (27%, 43/158) . CONCLUSION: There were significant variations among sites and geographical areas in terms of stroke imaging work-up used tomake decisions both for intravenous and endovascular revascularization. Clinical trials using advanced imaging as a selection tool for acute revascularization therapy should address the need for additional resources and technical support, and take into consideration the lack of routine use of such techniques in trial planning.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".