EP117 Virtual or Augmented Reality in Hip Arthroscopy Training Demonstrates Adequate Face, Content, and Construct Validity: A Systematic Review with Expert Commentary
Bibliographic record
Abstract
Abstract Purpose To summarize literature outlining the efficacy and validity of virtual reality (VR) and augmented reality (AR) simulation in hip arthroscopy training. Methods Three databases were searched on February 9th, 2025 for studies investigating the validity of VR/AR simulation for hip arthroscopy training. Data on participant details, simulation specifics, and validity (e.g. face, construct, and content) were included. Descriptive statistics were used to report results. Results Nine studies (one level I, one level II, seven level IV) comprising 218 participants were included, 37 (16.9%) being considered as “experts”. The majority of studies (7/9; 77.8%) were analyses of one session, with two studies comparing progress during sessions over time. Five studies investigated face validity, where the majority of participants (>70%) in each study found that the simulators were realistic (>7/10 or >6/7 via Likert scale) in all elements apart from tactile feedback. Eight studies evaluated construct validity. Four studies compared the safety performance of experts and non-experts, three (75%) finding statistically lower amounts of iatrogenic tissue damage in the expert group (p<=0.002). Three of four (75%) studies found that experts had lower completion times in either a diagnostic or surgical module (p<=0.03) than non-experts. One study reported lower iatrogenic damage via soft-tissue or bone collisions (p<0.0001) and completion times (p<0.001) in the seventh session of VR simulation, relative to the initial first three sessions in junior learners. Conclusion VR/AR simulation training for hip arthroscopy demonstrates adequate face, content, and construct validity. Generally, VR/AR simulations were rated realistic by users apart from tactile feel. Participants, particularly junior learners improved in minimizing iatrogenic tissue damage and decreasing completion times over multiple sessions. Experts generally outperformed non-experts in the majority of performance metrics, suggesting high construct validity.
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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 | 0.022 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".