Development and validation of a virtual reality transrectal ultrasound guided prostatic biopsy simulator
Notice bibliographique
Résumé
Surgical training has undergone some remarkable changes over the past 2 decades. No longer is the operating room the sole training ground for medical students and residents learning surgery. Basic skills, such as knot tying and laparoscopic suturing, can be practiced in the confines of a surgical skills laboratory.1 Studies have shown that ex-vivo surgical skills training can lead to significant improvement in actual intra-operative performance.2 Models used to teach learners have varied in fidelity, in terms of how realistic the model looks, from simple bench models to complex virtual reality (VR) model. However, a high fidelity or virtual reality model is not synonymous with an excellent teaching tool.3 The authors of this study have developed a VR model that captures the critical constructs of performing a transrectal ultrasound and biopsy (TRUS-BX).4 Targeting is the foundation of TRUS-BX and teaching a learner how to target is the focus of this simulator. The VR simulator developed by the University of Western Ontario group incorporates real patient 3D TRUS data to train and test a learner’s ability to target 12 virtual targets. The simulator calculates the accuracy of each of the biopsy taken and also records time. Face and content validity, through self-made questionnaire, showed this model to have a very realistic feel and simulation of a TRUS-BX. The VR TRUS-BX simulator was also able to discriminate performance between experts and novices. Furthermore, improvements were demonstrated while using this model. As the authors surmized, this VR simulator shows promise as a tool that can be used to train residents and serve a role in continuing professional development. We have seen great progress being made in surgical education research and advance in the science of technical skills assessment. However, there is also much more to be desired. As technology advances and increasingly more high fidelity VR simulators become available, there is a need for standardized methods in evaluating these new simulators. Measuring a simulators face and content validity needs to be more than experts’ opinion that “yes-this looks and feels like the real thing.” A stringent, reliable and accurate tool to measure face and content validity would allow meaningful comparison between simulator offerings from different companies. Measuring construct validity using subjects of varying experience has become a standard and acceptable method of simulator validation in the surgical education field. What would be highly desirable is a simulator with high predictive validity. Can the performance in the simulator predict the performance in the real-world? This would have significant implications on residency training and would introduce a high-stakes technical skills examination using simulator to attest to one’s competence. To achieve this goal, one needs to be able to measure technical performance in the operating room, which still remains the “holy grail” of surgical education. Ethical issues of live patients and the lack of unobtrusive and practical methods of intra-operative assessment remain impeding factors. Surgical education research remains a field that is evolving and it is encouraging to see well-thought simulators being designed and evaluated in a thorough manner.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».