Proposal for the Ischemic Stroke Phenotyping System 2025: ISPS25
Bibliographic record
Abstract
Over the last 30 years, several classification systems have been proposed or used to aid in patients with subtyping ischemic stroke based on its likely etiology. Since these classification systems have been published, the field has gained more knowledge about newly reported mechanisms (eg, carotid artery web), new treatments being effective (eg, patent foramen ovale closure), and new diagnostic testing (eg, prolonged outpatient cardiac monitoring). Therefore, an updated ischemic stroke classification system is needed to integrate these advancements, enabling a more targeted workup and potentially improving treatment strategies. We propose a more comprehensive but practical and generalizable diagnostic evaluation and checklist for patients with ischemic stroke to help identify definite, probable, or possible mechanisms related to cardioembolism, large artery atherosclerosis, small vessel disease, or other determined causes (eg, hypercoagulable disorders, genetic disorders, cancer, or dissection). This expands the minimum diagnostic evaluation, leading to reclassification of patients and possibly improving secondary stroke prevention strategies. This will also create a new framework for targeted treatments and randomized trials for various ischemic stroke subtypes.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".