HoloLens in suturing training
Bibliographic record
Abstract
PURPOSE: A training module for basic suturing training called Suture Tutor was developed by combining video instruction and voice commands with the Microsoft HoloLens software. We put forth two hypotheses: Trainees find the HoloLens helpful and 2.) HoloLens helps the trainees to achieve a better score in objective skill assessment tests. METHODS: Software module was developed to show instructional video in the HoloLens under voice command. Thirtytwo participants were split into the control group or the HoloLens group. The control group used videos displayed on a computer during training while the HoloLens group practiced with Suture Tutor. Each group was given seven minutes to train with their assigned training method before testing. Testing involved replication of a running locking suturing pattern with a time limit of five minutes and was video recorded. The videos were expert reviewed. Participants in the HoloLens group filled out a usability survey. RESULTS: The trainees found the Hololens to be usable and realistic, and the HoloLens group used the instructional videos more than the control group did (p = 0.0175). There was no difference in the skill assessment test scores between the HoloLens and the control group and their rates of completion in the allotted time was similar. CONCLUSION: Participants found the Suture Tutor to be a user friendly and helpful adjunct. The study was unable to determine if the Suture Tutor helps trainees in achieving a better score in skill assessment testing.
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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) | 0.002 | 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 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".