Systematic instruction of arthroscopic knot tying with the ArK Trainer: an objective evaluation tool
Bibliographic record
Abstract
PURPOSE: A Proficiency Formula was introduced as an objective self-evaluation method for evaluating basic arthroscopic knot tying in a laboratory setting. The correlation between the Proficiency Formula and gold standard pass/fail dichotomy was demonstrated, as well as with other popular evaluation tools--task-specific checklist (TSC) and global rating scale (GRS). METHOD: A step-by-step video tutorial was used to instruct 35 medical students on how to tie an arthroscopic Samsung Medical Center (SMC) knot secured by three half hitches. Participants were video recorded performing arthroscopic knot tying and assessed on their success tying an SMC knot, pass or fail, and through three outcome tools: the Proficiency Formula, GRS and the TSC. Independent samples t test was used to compare the GRS, TSC and Proficiency Formula scores, between those who were passed or failed by the evaluators. Correlation between the measurement scales was tested using Spearman's rho correlation coefficient. RESULTS: Participants received a mean proficiency score of 195 (140-249). The mean Proficiency score for those that passed was 323 (95 % CI 272-374), for those that failed, 87 (95 % CI 26-148, p < 0.001). We found strong linear correlation between the Proficiency Formula and GRS and TSE (0.83 and 0.78, respectively). CONCLUSION: The Proficiency Formula has high correlation with gold standard GRS and TSC measurements when used to assess arthroscopic knot tying skills on a model. It has the added advantage of being able to be self-assessed.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".