Shared Learning Utilizing Digital Methods in Surgery to Enhance Transparency in Surgical Innovation: Protocol for a Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Surgical innovation can lead to important improvements in patient outcomes. Currently, information and knowledge about novel procedures and devices are disseminated informally and in an unstandardized way (eg, through social media). This can lead to ineffective and inefficient knowledge sharing among surgeons, which can result in the harmful repetition of mistakes and delay in the uptake of promising innovation. Improvements are needed in the way that learning in surgical innovation is shared through the development of novel, real-time methods, informed by a contemporary and comprehensive investigation of existing methods. OBJECTIVE: The aim of this scoping review is to explore the application of existing digital methods for training/education and feedback to surgeons in the context of performing invasive surgical procedures. This work will (1) summarize existing methods for shared learning in surgery and how they are characterized and operationalized, (2) examine the impact of their application, and (3) explore their benefits and barriers to implementation. The findings of this scoping review will inform the development of novel, real-time methods to optimize shared learning in surgical innovation. METHODS: This study will adhere to the recommended guidelines for conducting scoping reviews. A total of 6 different searches will be conducted within multiple sources (2 electronic databases, journals, social media, gray literature, commercial websites, and snowball searches) to comprehensively identify relevant articles and data. Searches will be limited to articles published in the English language within the last 5 years. Wherever possible, a 2-stage study selection process will be followed whereby the eligibility of articles will be assessed through the title, abstract, and full-text screening independently by 2 reviewers. Inclusion criteria will be articles providing data on (1) fully qualified theater staff involved in performing invasive procedures, (2) one or more methods for shared learning (ie, digital means for training/education and feedback), and (3) qualitative or quantitative evaluations of this method. Data will be extracted (10% double data extraction by an independent reviewer) into a piloted proforma and analyzed using descriptive statistics, narrative summaries, and principles of thematic analysis. RESULTS: The study commenced in October 2021 and is planned to be completed in 2023. To date, systematic searches were applied to 2 electronic databases (MEDLINE and Web of Science) and returned a total of 10,093 records. The results of this scoping review will be published as open access in a peer-reviewed journal. CONCLUSIONS: This scoping review of methods for shared learning in surgery is, to our knowledge, the most comprehensive and up-to-date investigation that maps current information on this topic. Ultimately, efficient and effective sharing of information and knowledge of novel procedures and devices has the potential to optimize the evaluation of early-phase surgical research and reduce harmful innovation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37544.
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 | 0,117 | 0,116 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,005 |
| Méta-épidémiologie (sens large) | 0,011 | 0,014 |
| Bibliométrie | 0,016 | 0,013 |
| Études des sciences et des technologies | 0,006 | 0,006 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,005 | 0,008 |
| Intégrité de la recherche | 0,010 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,082 | 0,017 |
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 ».