The internal validity of arthroscopic simulators and their effectiveness in arthroscopic education
Bibliographic record
Abstract
PURPOSE: The purpose of this systematic review was to identify standard procedures for the validation of arthroscopic simulators and determine whether simulators improve the surgical skills of users. METHODS: Arthroscopic simulator validation studies and randomized trials assessing the effectiveness of arthroscopic simulators in education were identified from online databases, as well as, grey literature and reference lists. Only validation studies and randomized trials were included for review. Study heterogeneity was calculated and where appropriate, study results were combined employing a random effects model. RESULTS: Four hundred and thirteen studies were reviewed. Thirteen studies met the inclusion criteria assessing the construct validity of simulators. A pooled analysis of internal validation studies determined that simulators could discriminate between novice and experts, but not between novice and intermediate trainees on time of completion of a simulated task. Only one study assessed the utility of a knee simulator in training arthroscopic skills directly and demonstrated that the skill level of simulator-trained residents was greater than non-simulator-trained residents. CONCLUSIONS: Excessive heterogeneity exists in the literature to determine the internal and transfer validity of arthroscopic simulators currently available. Evidence suggests that simulators can discriminate between novice and expert users, but discrimination between novice and intermediate trainees in surgical education should be paramount. International standards for the assessment of arthroscopic simulator validity should be developed to increase the use and effectiveness of simulators in orthopedic surgery.
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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 | 0.044 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".