Exploration using holographic hands as a modality for skills training in medicine
Bibliographic record
Abstract
PURPOSE: Gaining proficiency in technical skills involving specific hand motions is prevalent across all disciplines of medicine and particularly relevant in learning surgical skills such as knot tying. We propose a new form of self-directed learning where a pair of holographic hands is projected in front of the trainee using the Microsoft HoloLens and guides them through learning various basic hand motions relevant to surgery and medicine. This study looks at the feasibility and effectiveness of using holographic hands as a skills training modality for learning hand motions compared to the traditional methods of apprenticeship and video-based learning. METHODS: 9 participants were recruited and each learned 6 different hand motions from 3 different modalities (video, apprenticeship, HoloLens). Results of successful completion and feedback on effectiveness was obtained through a questionnaire. RESULTS: Participants had a considerable preference for learning from HoloLens and apprenticeship and a higher success rate of learning hand motions compared to video-based learning. Furthermore, learning with holographic hands was shown to be comparable to apprenticeship in terms of both effectiveness and success rate. However, more participants still selected apprenticeship as a preferred learning method compared to HoloLens. CONCLUSION: This initial pilot study shows promising results for using holographic hands as a new effective form of self-directed apprenticeship learning that can be applied to learning a wide variety of skills requiring hand motions in medicine. Work continues toward implementing this technology in knot tying and suture tutoring modules in our undergraduate medical curriculum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".