The Knee Arthroscopy Learning Curve: Quantitative Assessment of Surgical Skills
Bibliographic record
Abstract
PURPOSE: To assess orthopaedic trainees performing diagnostic knee arthroscopies and evaluate procedural competence using a novel statistical method, the Cumulative Summation Test for Learning Curve (LC-CUSUM). METHODS: Twenty orthopaedic trainees in postgraduate year (PGY) 1 through 5 performed diagnostic knee arthroscopy and were evaluated intraoperatively with a validated 10-point knee task-specific checklist (TSCL) and 50-point global rating scale (GRS). A score of 40 points or greater (of 50 points) for the GRS and 8 points or greater (of 10 points) for the knee TSCL was considered to indicate a successful procedure. For the LC-CUSUM analysis, adequate performance was defined as a 10% failure rate, inadequate performance was defined as a 30% failure rate, and an acceptable deviation from adequate performance was defined as 10%. A limit h equal to 1.6 was selected to give a true-discovery rate of 90% and a false-discovery rate of 10% over 50 procedures. RESULTS: A total of 340 consecutive procedures were performed by 20 trainees during the 12-month study period. The cumulative number of arthroscopic procedures performed by trainees before study start increased with increasing PGY. The median number of arthroscopic procedures performed per trainee was 16.5 (interquartile range, 14 to 21.75). Competency in knee arthroscopy for the TSCL was achieved by 8 trainees (40%), after a median of 16 procedures (interquartile range, 13 to 20), and for the GRS by only a single trainee (5%), after 14 procedures. Threshold-adjusted curves stratified by PGY level enabled multiple trainees to achieve competency for both the TSCL and GRS. CONCLUSIONS: The LC-CUSUM can be successfully applied to knee arthroscopy to provide an individualized assessment of performance and quantitatively demonstrate competency for basic arthroscopic tasks. CLINICAL RELEVANCE: The LC-CUSUM is an effective method to evaluate procedure competence in arthroscopic training and can provide objective feedback and benchmarks in the learning phase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".