A Porcine Knee Model Is Valid for Use in the Evaluation of Arthroscopic Skills: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Previously validated knee arthroscopy evaluation tools have used human cadaveric knees. This is unsustainable because of the cost and scarcity of these specimens. Porcine (pig) knees are anatomically similar, affordable, and easily obtainable; however, whether porcine knees represent a suitable alternative to human specimens has not been evaluated. QUESTIONS/PURPOSES: The purpose of this study was to determine whether porcine knees are similar to human cadaveric knees for the assessment of knee arthroscopy skills by evaluating (1) the validity of the porcine model (whether trainees of the same level of ability scored similarly when using the two models) and (2) the reliability of the porcine model (whether surgeons with experience achieved higher scores than surgeons with less experience in the porcine model). METHODS: Eleven orthopaedic surgery residents (five junior residents and six senior residents), one orthopaedic sports medicine fellow, and three attending orthopaedic surgeons were enrolled. Participants were provided instructions for a proper arthroscopic examination of the knee and asked to identify, and then probe, the listed anatomic structures on both the human and porcine knee specimens. Each participant was asked to demonstrate the following skills: joint manipulation, instrument control and triangulation, fluid management, maintenance of field of view, economy of movement, and efficiency. The Objective Assessment of Arthroscopic Skills (OAAS) and checklist for diagnostic arthroscopy of the knee were used for skills assessment by one observer. Internal consistency, a measure of how well the assessment tool measures the skills being studied, was determined by Cronbach's α and group differences investigated by paired t-test and Wilcoxon signed-rank tests where appropriate. Based on a sample size calculation, a total of 37 subjects would be required for the full-scale research study to achieve a power of 0.80, with α set at 0.05, to detect a difference in OAAS score of 4.73 (25%). This value is outside of the 95% confidence intervals for the human knee. RESULTS: We found the porcine model to have a high level of face validity. There was no difference with the numbers available in total OAAS scores (mean ± SD; 95% confidence interval [CI]) within subjects between the human (18.93 ± 7.54; 14.76-23.11) and porcine (17.87 ± 6.36; 14.34-21.39) knees (p = 0.433). There was also no difference (p = 0.234) with the numbers available in overall OAAS score among participants working on either human (2.60 ± 1.35; 1.85-3.35) or porcine (2.33 ± 0.90; 1.84-2.83) specimens. Internal consistency of the simulation for both the human and porcine knees was high and did not differ between groups (Cronbach's α was 0.919 in the human knee and 0.954 in the porcine knee), suggesting the OAAS outcome score specifically assesses arthroscopic skill of participants in both the human and porcine models. More experienced arthroscopists scored higher than did less experienced trainees; there was high correlation (Pearson's correlation coefficient r, 95% CI) between years of experience and total OAAS scores in human (0.78; 0.46-0.92) and porcine (0.80; 0.49-0.93) diagnostic arthroscopy models. CONCLUSIONS: The porcine cadaveric knee model was a valid surrogate for the human knee in arthroscopic skills assessment. CLINICAL RELEVANCE: Trainees can be objectively evaluated using an affordable model that allows summative and formative feedback in the laboratory at a fraction of the cost of previously validated methods.
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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 | 0.025 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".