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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".