Approaches to the field recognition of potential thrombectomy candidates
Bibliographic record
Abstract
Systems of care for acute ischemic stroke are being challenged to implement processes that ensure rapid access to endovascular thrombectomy. Optimizing existing regionalized stroke thrombolysis programs for endovascular thrombectomy will require accurate field recognition of treatment candidates. We begin with a review of the development of early clinical tests for ischemic stroke, illustrating challenges relevant to future field tests for large vessel occlusion. Second, we discuss aspects of diagnosis, eligibility, feasibility, and system organization that are potentially relevant to the development and implementation of field tests and diversion criteria. These considerations may influence the choice and parametrization of field tests in individual jurisdictions. Third, we review the literature evaluating eight clinical tests for the field identification of probable large vessel occlusion. All candidate tests include evaluations for focal weakness, and six evaluate for cortical signs such as aphasia or gaze deviation. Most appear roughly comparable to the NIH Stroke Scale, but direct comparison between studies is inappropriate because of major methodological differences. Finally, we discuss our jurisdiction's approach to the field recognition of thrombectomy candidates. We contextualize diagnostic, eligibility, and system considerations within distinct metro and rural environments and propose a screen-and-consult model for the rural setting.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".