Imaging‐based selection in acute ischemic stroke trials – a quest for imaging sweet spots
Bibliographic record
Abstract
Ischemic stroke is a very heterogeneous disease that limits the efficacy of acute stroke treatments. Future trials will require advanced imaging to select patients for specific treatments. The most well-established imaging tools are the use of CT to exclude hemorrhage and diffusion-weighted MRI to demonstrate ischemia. While perfusion imaging is one option for patient selection, it has unresolved issues, including standardization and validation, that limit its value. As an alternative to mismatch when addressing stroke, one needs to know the size of the initial irreversible lesion (core), the presence and site/extent of occlusion (clot), and presence of leptomeningeal back filling and Willisian filling (collaterals). These can be summarized as the "3C" approach of core, clot, and collateral interpretation, which together can represent an imaging sweet spot, particularly for time-efficient endovascular treatment trial design.
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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 | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".