Validation of a Dry Model for Assessing the Performance of Arthroscopic Hip Labral Repair
Bibliographic record
Abstract
BACKGROUND: Arthroscopic hip labral repair is a technically challenging and demanding surgical technique with a steep learning curve. Arthroscopic simulation allows trainees to develop these skills in a safe environment. PURPOSE: The purpose of this study was to evaluate the use of a combination of assessment ratings for the performance of arthroscopic hip labral repair on a dry model. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: A total of 47 participants including orthopaedic surgery residents (n = 37), sports medicine fellows (n = 5), and staff surgeons (n = 5) performed arthroscopic hip labral repair on a dry model. Prior arthroscopic experience was noted. Participants were evaluated by 2 orthopaedic surgeons using a task-specific checklist, the Arthroscopic Surgical Skill Evaluation Tool (ASSET), task completion time, and a final global rating scale. All procedures were video-recorded and scored by an orthopaedic fellow blinded to the level of training of each participant. RESULTS: = 27.8, P < .001). A good correlation was seen between the ASSET score and previous exposure to arthroscopic procedures ( r = 0.52-0.73, P < .001). The interrater reliability for the ASSET score was excellent (>0.9). CONCLUSION: The results of this study demonstrate that the use of dry models to assess the performance of arthroscopic hip labral repair by trainees is both valid and reliable. Further research will be required to demonstrate a correlation with performance on cadaveric specimens or in the operating room.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.016 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".