Evaluating Simulation in Training for Arthroscopic Knee Surgery: A Systematic Review of the Literature
Bibliographic record
Abstract
PURPOSE: To evaluate the reported outcomes for measuring the effectiveness of simulation during knee arthroscopy training and determine the consistency of reporting and validation of simulation used in knee arthroscopy training. METHODS: Four databases (MEDLINE, Embase, CINAHL, and Cochrane Central Register of Controlled Trials) were screened for studies involving knee arthroscopy simulation training. Inclusion and exclusion criteria were applied to the searched studies, and a quality assessment was completed for included studies. The reviewers searched the references list in each of the eligible studies to identify other relevant studies that was not captured by our search strategy. RESULTS: We identified 13 eligible studies. The mean number of participants per study was 24 (range: 9 to 42 participants). The 3 most commonly reported surgical skills were the mean time to perform the task (100%), the visualization and probing tasks (77%), and the number of cartilage collisions with measurement of the surgical force (46%). The most commonly described measurement instruments included the Simulation Built-In Scoring System (54%), motion analysis system (23%), and Basic Arthroscopic Knee Skill Scoring System global rating scale (15%). The most frequently reported type of validity for the simulator was construct validity (54%) and concurrent validity (31%). Moreover, construct validity (69%) and concurrent validity (54%) were the most commonly reported type of validity for the measurement instrument. CONCLUSIONS: There is significant variation in reported learning outcomes and measurement instruments for evaluating the effectiveness of knee arthroscopic simulation-based education. Despite this, time to perform a task was the most commonly reported skill-evaluating outcome of simulation. The included studies in this review were of variable strength in terms of their evidence and methodologic quality. This study highlights the need for consistent outcome reporting after arthroscopic simulation training. LEVEL OF EVIDENCE: Level IV, systematic review of Level I, II, and IV studies.
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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 | 0.025 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".