ArticleJournal of applied clinical medical physics2020
Mitigating disruptions, and scalability of radiation oncology physics work during the COVID-19 pandemic.
Article in Journal of applied clinical medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Routine physics work efficacy and efficiency improvement through a shared-duty physicist-of-the-day model in a multi-hospital radiation oncology network.Journal of applied clinical medical physics · 2026Article
- Current status of remote radiotherapy treatment planning in Japan: findings from a national survey†.Journal of radiation research · 2024Article
- Shallow kinetics induced by a metronome (SKIM): A novel contactless respiratory motion management.Journal of applied clinical medical physics · 2023Article
- The impact of COVID-19 on a high-volume incident learning system: A retrospective analysis.Journal of applied clinical medical physics · 2022Article
- Telecommuting: A viable option for medical physicists amid the COVID-19 outbreak and beyond.Medical physics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe COVID-19 pandemic has led to disorder in work and livelihood of a majority of the modern world. In this work, we review its major impacts on procedures and workflow of clinical physics tasks, and suggest alternate pathways to avoid major disruption or discontinuity of physics tasks in the context of small, medium, and large radiation oncology clinics. We also evaluate scalability of medical physics under the stress of "social distancing".
methodsThree models of facilities characterized by the number of clinical physicists, daily patient throughput, and equipment were identified for this purpose. For identical objectives of continuity of clinical operations, with constraints such as social distancing and unavailability of staff due to system strain, however with the possibility of remote operations, the performance of these models was investigated. General clinical tasks requiring on-site personnel presence or otherwise were evaluated to determine the scalability of the three models at this point in the course of disease spread within their surroundings.
resultsThe clinical physics tasks within three models could be divided into two categories. The former, which requires individual presence, include safety-sensitive radiation delivery, high dose per fraction treatments, brachytherapy procedures, fulfilling state and nuclear regulatory commission's requirements, etc. The latter, which can be handled through remote means, include dose planning, physics plan review and supervision of quality assurance, general troubleshooting, etc.
conclusionAt the current level of disease in the United States, all three models have sustained major system stress in continuing reduced operation. However, the small clinic model may not perform if either the current level of infections is maintained for long or staff becomes unavailable due to health issues. With abundance, and diversity of innovative resources, medium and large clinic models can sustain further for physics-related radiotherapy services.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.