Bibliographic record
Abstract
Suturing is a basic skill required across several specialties. Medical students are most often taught this skill in a workshop setting with one faculty instructor demonstrating the technique. Often, students do not receive sufficient exposure and practice to reach proficiency during these sessions. As a result, there has been increased interest in self-directed suturing practice. Augmented reality, which involves the projection of virtual images in a user’s real environment, is an emerging tool in medical education. We sought to design and evaluate a training module, Suture Tutor, which combines video instruction with holography and voice control for self-directed suturing practice. We assessed the usability and effectiveness of Suture Tutor in a study conducted with 36 second-year medical students. The students were assigned to the Suture Tutor group or the control group. The Suture Tutor group used the training module on Microsoft HoloLens while the control group used the same instructional material on a laptop. Participants were asked to practice for seven minutes with their assigned training method. Then, they replicated a suturing pattern where their performance was video-recorded and evaluated. The Suture Tutor group completed a survey assessing the usability of the training module. It was found that the Suture Tutor was a user-friendly and helpful adjunct. Additionally, the Suture Tutor group interacted with the instructional material significantly more than the control group did (p = 0.0175), suggesting the use of Microsoft HoloLens increased access to training material. We were unable to make conclusions about the effectiveness of Suture Tutor.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".