Evaluating the role of endovascular simulation training in enhancing surgical performance metrics and patient outcomes in vascular surgery: A scoping review
Notice bibliographique
Résumé
Objective To assess the effectiveness of endovascular simulation training in enhancing surgical performance metrics and its influence on patient outcomes in vascular surgery. Methods A scoping review was conducted to explore the impact of simulation-based training in vascular surgery, with a specific focus on procedural metrics such as fluoroscopy time, radiation exposure, and contrast volume, as well as patient outcomes including perioperative complications, morbidity, and mortality. Comprehensive searches of Scopus, OVID Medline, and OVID Embase were performed using structured query strings encompassing terms related to endovascular simulation, training methods, and measurable clinical and procedural outcomes. Screening and selection adhered to the PRISMA-ScR guidelines. Studies were included if they assessed simulation-based training for endovascular procedures and reported measurable technical and patient-centered outcomes; reviews, commentaries, and studies not involving endovascular simulation or relevant metrics were excluded. Data were extracted on study characteristics, simulation modalities, clinical endpoints, and procedural performance metrics, and the findings were synthesized to identify trends in the literature. Results Six studies met the inclusion criteria, utilizing a variety of simulation modalities, including virtual reality, 3D-printed models, and patient-specific rehearsal. Simulation training was associated with significant improvements in procedural metrics during real and simulated procedures, including reductions in fluoroscopy time, procedure duration, radiation exposure, and contrast volume. Improvements in technical proficiency and operator confidence were consistently observed across studies. However, the evidence linking simulation to direct patient-specific outcomes, such as reduced perioperative complications or mortality, was limited. While two studies demonstrated statistically significant improvements in clinical outcomes, others showed trends without statistical significance, and two studies found no measurable impact on patient outcomes. Conclusions Simulation-based training enhances procedural efficiency, technical performance, and operator confidence in vascular surgery. However, direct evidence linking simulation training to improved patient outcomes remains inconclusive. Future research should focus on high-quality, multicenter randomized controlled trials with standardized outcome measures to better establish the clinical value of simulation training and inform its widespread integration into vascular surgery education and training programs.
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 | 0,045 | 0,193 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,009 |
| Bibliométrie | 0,026 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».