Performance Assessment of Arthroscopic Rotator Cuff Repair and Labral Repair in a Dry Shoulder Simulator
Bibliographic record
Abstract
PURPOSE: To evaluate the use of dry models to assess performance of arthroscopic rotator cuff repair (RCR) and labral repair (LR). METHODS: Residents, fellows, and sports medicine staff performed an arthroscopic RCR and LR on a dry model. Any prior RCR and LR experience was noted. Staff surgeons assessed participants by use of task-specific checklists, the Arthroscopic Surgical Skill Evaluation Tool (ASSET), and a final overall global rating. All procedures were video recorded and were scored by a fellow blinded to the year of training of each participant. RESULTS: A total of 51 participants and 46 participants performed arthroscopic RCR and LR, respectively, on dry models. The internal consistency or reliability (Cronbach α) using the total ASSET score for the RCR and LR was high (>0.9). One-way analysis of variance for the total ASSET score showed a difference between participants based on year of training (P < .001) for both procedures. The inter-rater reliability for the ASSET score was excellent (>0.9) for both procedures. A good correlation was seen between the ASSET score and the year of training, as well as the previous number of sports rotations. CONCLUSIONS: The results of this study show evidence of construct validity when using dry models to assess performance of arthroscopic RCR and LR by residents. CLINICAL RELEVANCE: The results of this study support the use of arthroscopic simulation in the training of residents and fellows learning arthroscopic shoulder surgery.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".