Module‐Based Arthroscopic Knee Simulator Training Improves Technical Skills in Naive Learners: A Randomized Trial
Bibliographic record
Abstract
Purpose To compare the effectiveness, in comparison to a control group (C), of module‐based training (MBT) and traditional learning (TL) as a means of acquiring arthroscopic skills on an arthroscopic surgery simulator. Methods Thirty health sciences students with no previous arthroscopy experience were recruited and randomized into 1 of 3 groups: MBT, TL, or C (1:1:1 ratio). Participants in MBT were required to independently practice on a VirtaMed ArthroS simulator (VirtaMed AG, Zurich, Switzerland) for a minimum of 2 hours per week, whereas TL received one‐on‐one coaching by a senior orthopaedic resident for 15 minutes per week. The control group received no training. All groups were assessed at baseline and at 4 weeks based on objective measures generated by the surgical simulator (procedure time, camera path length, meniscus cutting score, detailed visualization, safety score and total score), and subjective ratings scales (Objective Assessment of Arthroscopic Skill [OAAS] global assessment form, and Competency‐Based Assessment form). Results Participants in the MBT group trained on average 113 min/week whereas the TL group trained on average 24 min/week. Three‐way repeated‐measures analysis of variance showed significant group by time interactions for procedure time ( P = .006), camera path length ( P = .008), safety score ( P = .013), total score ( P = .003), OAAS form ( P < .001), and Competency‐Based Assessment form ( P < .001). MBT group was superior to C group for procedure time ( P = .02), camera path length ( P = .003), total score ( P = .004), and OAAS form ( P = .021), but there were no significant post‐hoc differences between MBT and TL groups, or TL and C groups after Bonferroni correction. Total practice time explained 37.5% of the final simulator total score variance. Conclusions Knee arthroscopy simulation training with self‐learning modules can improve skills in areas such as procedure time, camera path length, and total score in untrained participants compared with a control group. Clinical Relevance Module‐based simulation training provides additional training time and improves technical skills in naive health science students. It is hoped that this effect can be preserved and applied to junior resident developing in a busy residency program.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".