A Randomized Trial of Telemedicine Models of Care on a Mobile Stroke Unit
Bibliographic record
Abstract
BACKGROUND: Mobile stroke units (MSUs) accelerate prehospital acute stroke care and improve outcomes. Both onboard and telemedicine neurologist models of care are used but have not been directly compared. METHODS: MSU-TELEMED was a randomized, open-label, blinded-endpoint trial comparing onboard neurologist care to a telemedicine care model for people presenting to an MSU with suspected stroke. MSU care was prospectively randomized by day to onboard versus telemedicine care. The primary outcome was a hierarchical composite outcome using a win-odds approach that prioritized: (1) safety, (2) scene-to-treatment-decision time, and (3) percentage of the total case time the neurologist spent in direct care (higher values denote better resource use). Every participant in each group was compared to those in the other, resulting in a "win/tie/loss" distribution for telemedicine compared to onboard. RESULTS: A total of 275 participants were assigned to telemedicine (n=135) or onboard (n=140) neurologist care groups. The primary outcome of win/tie/loss distribution favored the telemedicine model (76%/4%/20%) with an adjusted win odds of 3.5 (95% confidence interval [CI], 2.4-5.1). Safety events were similar (13% telemedicine vs. 12% onboard, risk ratio 0.9; 95% CI, 0.5-1.8). Median scene-to-treatment-decision time was 19 minutes in the telemedicine group and 13 minutes in the onboard group (adjusted difference in median time 4 minutes; 95% CI, 1.9-5.9). The median percentage of the neurologist's time directly involved in patient care was 100% in the telemedicine group and 33% in the onboard group (adjusted difference in median percentage 63 percentage points; 95% CI, 53-74). CONCLUSIONS: Compared to an onboard model, an MSU telemedicine model of care was superior based on a composite hierarchical outcome of safety, scene-to-treatment-decision time, and percentage of the neurologist's time spent in direct care. (Funded by the Sylvia and Charles Viertel Charitable Foundation and the Medical Research Future Fund "Golden Hour"; ClinicalTrials.gov number, NCT05991310.).
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".