Cognitive priority model for advanced telemedical support in Limited Bandwidth Applications
Bibliographic record
Abstract
Telemedicine offers the ability to provide real-time medical support, education, and care to remote and austere locations under limited bandwidth restrictions. These types of communication channels can easily be overwhelmed and delays or interruptions make communication difficult, if not impossible. These interruptions destroy the temporal orientation of communication resulting in significant cognitive loading. The objective of this research was to lower cognitive load and minimize digital communication bandwidth by developing a priority model from perceptual quality and content focus of telemedical video. H.264/AVC encoding was used to encode two types of medical context with varying bitrates, frame rates, and frame sizes. Telemedical video contexts were room awareness, and medical procedure. Objective quality and subjective quality tests were performed using Structural Similarity (SSIM) and perceptual feedback, respectively. The objective of this research is to develop context specific telemedicine communication models for the highest perceptual quality for available bandwidth for the purpose of increased temporal orientation and decreased cognitive load. Our research presents a method to select the best encoding parameters (maximum bitrate, frame rate and frame size) for the medical context to minimize bandwidth and maintain diagnostic and education quality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".