Immersive Extended Reality use in Medical Education with Implications for Remote and Space Medicine Training
Bibliographic record
Abstract
Objective: Medical education continually adapts and evolves as evidence-based practice expands our knowledge. Ease of access and reduced cost of novel technology has revolutionized delivery of medical education. Immersive extended reality (iXR) technology may play an invaluable role in improving delivery and quality of medical education in rural and extra-planetary settings. Methods: An extensive literature review was conducted for using MeSH terms specific to iXR technology related to medical education. Relevant manuscripts were accessed from database searches and filtered based on pre-specified inclusion and exclusion criteria. Extraction of data and thematic qualitative analysis was conducted in relation to relevance to remote medical training with an emphasis on potential for deep-space exploration class missions. Results: From a total of 4005 search results, 35 final papers met inclusion criteria for this study. Current applications of virtual, augmented and mixed reality technology in medical education were explored, and themes from each modality of iXR technology were defined. Conclusions: Themes determined from the results were applied to a discussion regarding the application of iXR technology in two distinct areas: remote and rural medical training and space medicine. Relative strengths and weaknesses of each modality of iXR were explored and applied to the unique factors impacting medical education delivery in these two domains.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".