Global Survey of the Diagnostic Evaluation and Management of Cryptogenic Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: About 25% of ischemic strokes are categorized as cryptogenic (i.e. of unknown cause), but few data exist about the extent of diagnostic testing or treatment. We undertook an international survey to characterize current diagnostic evaluation and antithrombotic management of patients with cryptogenic ischemic stroke in 2014. AIMS/HYPOTHESIS: To determine the type of diagnostic evaluation undertaken for cryptogenic ischemic stroke and antithrombotic management and to compare across global regions. METHODS: An 18-question online survey was sent to 995 physicians involved in stroke care in 61 countries. Countries were separated into World Bank global regions and income groups. Diagnostic tests were considered routine if performed in >75% of patients at a center. RESULTS: Three hundred one completed surveys were received from 48 countries (response rate ∼30%). The majority (82%) of hospitals were from high-income countries and mainly from Europe and Central Asia (56%) and North America (19%). For ischemic stroke patients, magnetic resonance imaging is routinely obtained at 36% of hospitals (highest in North America, 58%). Among cryptogenic stroke patients, transesophageal echocardiography is routinely performed in 17% of hospitals. More than 24 hour cardiac rhythm monitoring is done routinely at relatively few (17%) hospitals (highest in North America, 33%). Intracranial arterial imaging is done routinely at 70% of hospitals, with no significant regional differences. Antiplatelet therapies are routinely prescribed for secondary prevention at 94% of hospitals. CONCLUSIONS: Based on self-selected respondents from a large number of international stroke centers, transesophageal echocardiography and prolonged (>24 h) cardiac rhythm monitoring are not routinely performed in cryptogenic stroke patients, even in high-income countries. Antiplatelet therapy is the global standard for secondary prevention of cryptogenic stroke.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".