A Porcine Knee Model Is Valid for Use in the Evaluation of Arthroscopic Skills: A Pilot Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,025 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».