Evaluating the Safety and Efficacy of Telemedicine Physician Assessments on a Mobile Stroke Unit: Protocol for a Prospective Open‐Label Blinded End‐Point Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mobile stroke units have been shown to deliver faster patient care and improve clinical outcomes. However, costs associated with staffing limit their use to densely populated cities. Using the Melbourne mobile stroke unit, we aim to evaluate the safety, timeliness, and resource efficiency of a telemedicine model, where the neurologist assesses a patient remotely, via telemedicine, compared with an onboard neurologist model. We hypothesize that, without compromising patient safety, the telemedicine model will provide timely care and superior resource efficiency. METHODS: Using a prospective, randomized, blinded end-point controlled design, 270 participants consecutively assessed on the Melbourne mobile stroke unit over ≈12 months will be assigned into 2 arms: (1) telemedicine neurologist assessment (intervention) versus (2) onboard assessment (comparator). Enrollment is based on prospectively designated randomized days of neurologist review onboard versus telemedicine. The primary outcome will be the odds that a randomly selected participant in the telemedicine arm will have a better outcome than a randomly selected participant in the onboard arm, measured using a desirability-of-outcome ranking, an outcome measure that includes, in order of importance: (1) safety, (2) scene-to-treatment-decision time metrics, and (3) resource usage. All participants within each arm will be compared with those in the other, resulting in a "win/tie/loss" distribution for telemedicine compared with the onboard model. CONCLUSIONS: The study will establish whether use of a telemedicine neurologist delivers superior resource efficiency without compromising patient care. This would enable the broader use of mobile stroke units, particularly relevant to regions with limited access to neurologists, thus improving equity in access to time-critical, lifesaving stroke care. REGISTRATION: URL: clinicaltrials.gov; Unique Identifier: 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 | 0.064 | 0.063 |
| Meta-epidemiology (narrow) | 0.009 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".