Guidelines for TMS/tES clinical services and research through the COVID-19 pandemic
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has broadly disrupted biomedical treatment and research including non-invasive brain stimulation (NIBS). Moreover, the rapid onset of societal disruption and evolving regulatory restrictions may not have allowed for systematic planning of how clinical and research work may continue throughout the pandemic or be restarted as restrictions are abated. The urgency to provide and develop NIBS as an intervention for diverse neurological and mental health indications, and as a catalyst of fundamental brain research, is not dampened by the parallel efforts to address the most life-threatening aspects of COVID-19; rather in many cases the need for NIBS is heightened including the potential to mitigate mental health consequences related to COVID-19. OBJECTIVE: To facilitate the re-establishment of access to NIBS clinical services and research operations during the current COVID-19 pandemic and possible future outbreaks, we develop and discuss a framework for balancing the importance of NIBS operations with safety considerations, while addressing the needs of all stakeholders. We focus on Transcranial Magnetic Stimulation (TMS) and low intensity transcranial Electrical Stimulation (tES) - including transcranial Direct Current Stimulation (tDCS) and transcranial Alternating Current Stimulation (tACS). METHODS: The present consensus paper provides guidelines and good practices for managing and reopening NIBS clinics and laboratories through the immediate and ongoing stages of COVID-19. The document reflects the analysis of experts with domain-relevant expertise spanning NIBS technology, clinical services, and basic and clinical research - with an international perspective. We outline regulatory aspects, human resources, NIBS optimization, as well as accommodations for specific demographics. RESULTS: A model based on three phases (early COVID-19 impact, current practices, and future preparation) with an 11-step checklist (spanning removing or streamlining in-person protocols, incorporating telemedicine, and addressing COVID-19-associated adverse events) is proposed. Recommendations on implementing social distancing and sterilization of NIBS related equipment, specific considerations of COVID-19 positive populations including mental health comorbidities, as well as considerations regarding regulatory and human resource in the era of COVID-19 are outlined. We discuss COVID-19 considerations specifically for clinical (sub-)populations including pediatric, stroke, addiction, and the elderly. Numerous case-examples across the world are described. CONCLUSION: There is an evident, and in cases urgent, need to maintain NIBS operations through the COVID-19 pandemic, including anticipating future pandemic waves and addressing effects of COVID-19 on brain and mind. The proposed robust and structured strategy aims to address the current and anticipated future challenges while maintaining scientific rigor and managing risk.
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 | 0,103 | 0,186 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,010 | 0,007 |
| Science ouverte | 0,012 | 0,010 |
| Intégrité de la recherche | 0,028 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,019 |
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 ».