Abstract TP250: Defining Technical Quality Standards for Mobile Telestroke Assessment: A Substudy of the Improving Treatment with Rapid Evaluation of Acute Stroke via Mobile Telemedicine (iTREAT) Mobile Telestroke Platform
Bibliographic record
Abstract
Introduction: In-ambulance use of remote videoconferencing for prehospital stroke assessment (mobile telestroke) is an emerging innovation in acute stroke care. As a new technology, there is a dearth of technical standards to ensure transmission quality and guide deployment in various EMS settings. Hypothesis: Subjective video quality ratings during in-vehicle mobile telestroke assessment correlate to objective video data transmission metrics. Methods: We performed videoconferencing via a low-cost, utilitarian mobile telestroke platform: tablet endpoint, high-speed 4G LTE modem, external antennae, HIPAA-secure videoconferencing application, and portable bracket mounting. We held test calls along typical ambulance routes recording transmission quality by a stationary and a mobile rater. We used a standardized 6-point scale of video quality: rating ≥ 4 deemed acceptable for mobile telestroke assessment. We recorded jitter, the variance in transmission data reception order, as simultaneously reported by the videoconferencing application. Results: We completed five test runs yielding 64 data ratings. Average jitter for ratings 1 through 6 was 434.9ms (SD = 407), 106.1ms (SD = 110), 41.4ms (SD = 29), 35.3ms (SD = 15), 29.5ms (SD = 6), and 29.0ms (SD = 2) respectively. Analyzing the raw data yielded an R2 of 0.41. As seen in Chart 1, video quality decreased as average jitter increased, but jitter values as low as 30ms were still seen across video transmission of all qualities. Conclusion: These preliminary data suggest modest correlation of transmission variance with subjective quality ratings using a low-cost mobile telestroke platform along rural-based ambulance routes. However, average transmission variance correlated highly (R2 = 0.93) suggesting more data ratings may improve the correlation. Testing of our mobile telestroke platform to assess performance and clinical efficacy as well as incorporate live acute stroke encounters is ongoing.
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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 | 0.008 | 0.019 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".