Leveraging Space-Flown Technologies to Deliver Healthcare with Holographic Physical Examinations
Bibliographic record
Abstract
INTRODUCTION: Musculoskeletal injuries are one of the more common injuries in spaceflight. Physical assessment of an injury is essential for diagnosis and treatment. Unfortunately, when musculoskeletal injuries occur in space, the flight surgeon is limited to two-dimensional videoconferencing and, potentially, observations made by the crew medical officer. To address these limitations, we investigated the feasibility of performing physical examinations on a three-dimensional augmented reality projection using a mixed-reality headset, specifically evaluating a standard shoulder examination. METHODS: A simulated patient interaction was set up between Western University in London, Ontario, Canada, and Huntsville, AL, United States. The exam was performed by a medical student, and a healthy adult man volunteered to enable the physical exam. RESULTS: All parts of the standard shoulder physical examination according to the Bates Guide to the Physical Exam were performed with holoportation. Adaptation was required for the palpation and some special tests. DISCUSSION: All parts of the physical exam were able to be completed. The true to anatomical size of the holograms permitted improved inspection of the anatomy compared to traditional videoconferencing. Palpation was completed by instructing the patient to palpate themselves and comment on relevant findings asked by the examiner. Range of motion and special tests for specific pathologies were also able to be completed with some modifications due to the examiner not being present to provide resistance. Future work should aim to improve the graphics, physician communication, and haptic feedback during holoportation. Levschuk A, Whittal J, Trejos AL, Sirek A. Leveraging space-flown technologies to deliver healthcare with holographic physical examinations. Aerosp Med Hum Perform. 2024; 95(4):214–218.
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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 | 0.002 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".