Recognition of intraoperative surgical glove perforation: a comparison by surgical role and level of training
Notice bibliographique
Résumé
BACKGROUND: The aim of this study was to characterize the risk of glove perforation among surgical team members performing a typical set of trauma procedures, as well as to identify the rate at which these people recognize potential perforations. METHODS: Gloves used in orthopedic trauma room procedures were collected from all participating team members over 2 weeks and were subsequently examined for perforations. Perforation rates based on glove position, type, wearer and procedure were assessed. RESULTS: Perforations were found in 5.9% of gloves; 4.3% of the perforations were found in outer gloves and 1.6% in inner gloves. Among the outer gloves, 30.7% of the perforations were recognized by the wearer at the time of perforation; none of the inner glove perforations were recognized, even when they were associated with an accompanying outer glove perforation. Significantly more perforations were identified in the gloves of attending staff than in those of other team members. Attending staff experienced more perforations than other wearers, regardless of whether they were acting as the primary surgeon or as an assistant. Perforations were more common in open reduction internal fixation and amputation procedures. For open reduction internal fixation procedures, longer operative times were associated with more frequent glove perforations. CONCLUSION: The rates of glove perforation are high in orthopedic trauma surgeries, and often these perforations are not recognized by the wearer. Attending staff are at an elevated risk of glove perforation. It is recommended that all members of the surgical team change both pairs of gloves whenever an outer glove perforation is observed.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».