A Randomized Trial of Telemedicine Models of Care on a Mobile Stroke Unit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it