Mixed-Methods Analysis of a Validated Arthroscopic Knot-Tying Simulator With New Indirect Visualization Condition
Bibliographic record
Abstract
Background: Simulation provides low-risk opportunities for surgical trainees to learn and practice fundamental skills. One simulation tool for orthopaedics is the Arthroscopic Knot (ArK) Trainer, which has been validated as an effective simulation tool across multiple methodologies. Previous studies have investigated the ArK Trainer in its basic form using clear plexiglass, which allows direct visualization of tissue anchors. Purpose: Using a mixed-methods approach, we assessed and compared junior and senior trainees’ Seoul Medical Center (SMC) knot–tying performance under direct and indirect visualization. Study Design: Cross-sectional study. Methods: Fourteen orthopaedic surgery postgraduate trainees at a single medical school were recruited to participate. Trainees tied SMC knots using the Ark Trainer under direct and indirect visualization. A mixed-methods approach was used to evaluate knot-tying proficiency and characterize participants’ approach to knot-tying. Knot-tying proficiency was evaluated using validated tools: a task-specific checklist (TSC), a global rating scale (GRS), and a proficiency scale (PS). Participants’ approach to knot-tying was characterized using Likert-type questionnaires and semistructured interviews. An α level of .10 was set a priori owing to the small pool of trainees. Results: The 14 participants included 7 junior residents (postgraduate years [PGYs] 1 and 2) and 7 senior residents (PGY ≥3), of whom 3 were fellows (PGY 6). Senior trainees outperformed junior trainees on both versions of the ArK Trainer: clear (GRS, P = .055; PS, P = .075) and covered (TSC, P = .05). Overall, participants performed better under direct visualization conditions (GRS, P = .05). In semistructured interviews, significantly more senior trainees discussed relying on haptic cues while tying knots under direct visualization ( P = .021). The majority of trainees agreed that both versions of the ArK Trainer were realistic and appropriate practice formats for their level of training. Conclusion: Senior trainees were significantly more experienced than were junior trainees in arthroscopic skill and outperformed them on both configurations: direct (PS and GRS) and indirect (TSC) visualization. Experienced trainees were significantly more likely to report using tactile cues to aid knot-tying under indirect visualization. It is likely that inexperienced trainees rely more heavily on direct visualization and that the use of tactile cues may be an indicator of knot-tying proficiency. Trainees recommended progression from direct to indirect visualization configurations for inexperienced learners.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".