Work-in-Progress: A Novel Data Glove for Psychomotor-Based Virtual Medical Training
Bibliographic record
Abstract
Despite its importance in the real-world, manual (hand) dexterity is often ignored in medical-based virtual training environments that have traditionally focused on cognitive and affective skills development. Psychomotor (technical) skills, particularly those related to manual dexterity, are fundamental to various medical procedures and ignoring them in virtual based training tools can lead to a sub-optimal training experience. Here, we present a novel, consumer-level data glove that provides accurate user interactions involving the proximal and medial phalanges, interactions that are relevant in many manual dexterity tasks. We also outline how this novel data glove is being incorporated into an existing serious gaming platform for anesthesia training that currently focuses on cognitive and affective skills development only. The addition of psychomotor skills development through the adoption of simulated tactile feedback will provide a more complete serious gaming training platform.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".