SIMULATION OF ARTHROSCOPIC ROTATOR CUFF REPAIR AND LABRAL REPAIR IN A DRY MODEL
Bibliographic record
Abstract
The purpose of this study was to validate a dry model for the assessment of performance of arthroscopic rotator cuff repair (RCR) and labral repair (LR). We hypothesised that the combination of a checklist and a previously validated global rating scale (GRS) would be a valid and reliable means of assessing RCR and LR when performed by residents in a dry model. An arthroscopic RCR and LR was performed on a dry model by residents, fellows, and sports medicine staff. Any prior RCR and LR exposure was noted. Participants were given a detailed surgical manuscript and technique video before the study began. Evaluation of residents was performed by staff surgeons with task-specific checklists created using a modified Delphi procedure, and the Arthroscopic Surgical Skill Evaluation Tool (ASSET). The hand movements and arthroscopic view of the procedures were recorded. Both videos were scored by a fellow blinded to the year of training of each participant. A total of 35 residents, six fellows and five staff surgeons performed both arthroscopic RCR and LR on a dry model model (48 total). The internal reliability (Cronbach9s Alpha) of the test using the total ASSET score was high (>0.8)). One-way analysis of variance for the total ASSET score and the total checklist score demonstrated a difference between participants based upon year of training (p The results of this study provide evidence that the performance of a RCR and LR in a dry model is a valid and reliable method of assessing a resident9s ability to perform these procedures, prior to performance in the operating room.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".