Construct validity testing of the Arthroscopic Knot Trainer (ArK)
Bibliographic record
Abstract
PURPOSE: This study introduced a novel simulator called the Arthroscopic Knot Trainer (ArK) and reports preliminary evidence to support its construct validity. To our knowledge, the ArK is the first non-anatomical tissue reduction simulator designed to meet learning objectives specific for developing knot-tying skills. MATERIALS AND METHODS: A step-by-step instructional video was used to teach orthopaedic residents how to tie an arthroscopic SMC knot. Residents were video recorded to assess time of completion, number of knots tied in 10 min and re-assessed 6 months later. Subjects were surveyed for content evidence after using the ArK. Data were analysed by paired t test and independent sample t test in order to compare the mean time to tie knots from test at baseline to retest at 6 months and the between group mean time, respectively. RESULTS: Content evidence supports the ArK trainer as appropriate for teaching and assessing arthroscopic knot-tying skills. Relation to other variables evidence supports the ArK trainer model whether stratified by year of training or by self-reported experience; time required for knot tying was inversely correlated with experience in tying arthroscopic knots. Internal structure evidence was supported with similar findings at retesting. CONCLUSIONS: There are three sources of evidence supporting the construct validity of the ArK as a simulator for arthroscopic knot tying: content, relationship to other variable and internal structure evidence. The ArK is easy to use and has the capacity to distinguish between groups with different skill levels.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".