A Validated Orthopaedic Surgical Simulation Model for Training and Evaluation of Basic Arthroscopic Skills
Bibliographic record
Abstract
BACKGROUND: To our knowledge, there is currently no validated educational model to evaluate and teach basic arthroscopic skills that is widely accessible to orthopaedic residency training programs. The primary objective was to design and to validate a surgical simulation model by demonstrating that subjects with increasing level of training perform better on basic arthroscopic simulation tasks. The secondary objective was to evaluate inter-rater and intra-rater reliability of the model. METHODS: Prospectively recruited participants were divided by level of training into four groups. Subjects performed six basic arthroscopic tasks using a box model: (1) probing, (2) grasping, (3) tissue resection, (4) shaving, (5) tissue liberation and suture-passing, and (6) knot-tying. A score was calculated according to time required to complete each task and deductions for technical errors. A priori total global score, of a possible 100 points, was calculated by averaging scores from all six tasks using equal weights. RESULTS: A total of forty-nine participants were recruited for this study. Participants were grouped by level of training: Group 1 (novice: fifteen medical students and interns), Group 2 (junior residents: twelve postgraduate year-2 or postgraduate year-3 residents), Group 3 (senior residents: sixteen postgraduate year-4 or postgraduate year-5 residents), and Group 4 (six arthroscopic surgeons). The mean total global score (and standard deviation) differed significantly between groups (p < 0.001): 29.0 ± 13.6 points for Group 1, 40.3 ± 12.1 points for Group 2, 57.6 ± 7.4 points for Group 3, and 72.4 ± 3.0 points for Group 4. Pairwise comparison with Tukey correction confirmed construct validity by showing significant improvement in overall performance by increasing level of training between all groups (p < 0.05). The model proved to be highly reliable with an intraclass correlation coefficient of 0.99 for both inter-rater and intra-rater reliability. CONCLUSIONS: A simulation model was successfully designed to teach and evaluate basic arthroscopic skills showing good construct validity. This arthroscopic simulation model is inexpensive, valid, and reliable and has the potential to be implemented in other training programs.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".