Telestroke network to robotic telestroke network: How to upgrade regional stroke care to include remote robotics?
Bibliographic record
Abstract
Objective: Selected patients with large vessel occlusion (LVO) strokes can benefit from endovascular therapy (EVT). However, the effectiveness of EVT is largely dependent on how quickly the patient receives treatment. Recent technological developments have led to the first neurointerventional treatments using robotic assistance, opening up the possibility of performing remote stroke interventions. Existing telestroke networks provide acute stroke care, including remote administration of intravenous thrombolysis (IVT). Therefore, the introduction of remote EVT in distant stroke centers requires an adaptation of the existing telestroke networks. The aim of this work was to propose a framework for centers that are potential candidates for telerobotics according to the resources currently available in these centers. Methods: In this paper, we highlight the future challenges for including remote robotics in telestroke networks. A literature review provides potential solutions. Results: Existing telestroke networks need to determine which centers to prioritize for remote robotic technologies based on objective criteria and cost-effectiveness analysis. Organizational challenges include regional coordination and specific protocols. Technological challenges mainly concern telecommunication networks. Conclusions: Specific adaptations will be necessary if regional telestroke networks are to include remote robotics. Some of these can already be put in place, which could greatly help the future implementation of the technology.
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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 | 0.008 |
| 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 | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".