Guidelines for TMS/tES clinical services and research through the COVID-19 pandemic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.028 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".