Organizing stroke systems in the field for patients with suspected large vessel occlusion acute stroke
Bibliographic record
Abstract
Introduction: The dawn of endovascular stroke therapy has reshaped stroke care. Eligible patients need to be rushed to capable centers for intervention. This may entail bypassing closer hospitals that could confirm the diagnosis, administer thrombolytic therapy, then transfer patients for intervention. This has created a set of challenges: identifying endovascular candidates in the field, determining the best transport destination, and getting patients there quickly.Areas covered: This review provides a context for these emerging challenges. Current and emerging clinical prediction instruments for large vessel occlusion (LVO) are reviewed. The workflow in the thrombolysis-only primary stroke centers is reviewed, and interventions aimed at minimizing delays are highlighted. Innovations using mathematical modeling and devices for detection of LVO are reviewed.Expert commentary: More patients are expected to receive endovascular therapy as we push the boundaries for time and imaging criteria. Advances in detection and decision-making aids will improve the speed of treatment. Some patients will arrive at thrombolysis-only centers. This need to be triaged, diagnosed, treated, and transported promptly. Therefore, education of practitioners in these centers is paramount. Creating and facilitating infrastructure for imaging acquisition and sharing in such centers will reflect better care for stroke patients overall.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".