ArticleCureus2022
Practical Implementation of Emergent After-Hours Radiation Treatment Process Using Remote Treatment Planning on Optimized Diagnostic CT Scans.
Article in Cureus, 2022. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Clinical Implementation of Simulation-Free Palliative Radiation Therapy: Consensus Recommendations From a Modified Delphi Study.Advances in radiation oncology · 2026Article
- Simulation-Free Palliative Radiation Therapy: Implementing the Value-Based and Easier-Access Model of Care in a Rural Setting.Journal of medical radiation sciences · 2025Review
- Development of an automated CBCT-based simulation-free platform for expedited palliative radiotherapy on a conventional linear accelerator.Journal of applied clinical medical physics · 2025Article
- Simulation-free magnetic resonance-guided radiation therapy of prostate cancer.Physics and imaging in radiation oncology · 2024Article
- Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review.Clinical Medicine Insights. Oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this report is to present the implementation of a process for after-hours radiation treatment (RT) utilizing remote treatment planning based on optimized diagnostic computed tomography (CT) scans for the urgent palliative treatment of inpatients. A standardized operating procedure was developed by an interprofessional panel to improve the quality of after-hours RT and minimize the risk of treatment errors. A new diagnostic CT protocol was created that could be performed after-hours on hospital scanners and would ensure a reproducible patient position and adequate field of view. An on-call structure for dosimetry staff was created utilizing remote treatment planning. The optimized CT protocol was developed in collaboration with the radiology department, and a novel order set was created in the electronic health system. The clinical workflow begins with the radiation oncologist notifying the on-call team (therapist, dosimetrist, and physicist) and obtaining an optimized diagnostic CT scan on a hospital-based scanner. The dosimetrist remotely creates a plan; the physicist checks the plan; and the patient is treated. Plans are intentionally simple (parallel opposed fields, symmetric jaws) to expedite care and reduce the risk of error. Education on the new process was provided for all relevant staff. Our process was successfully implemented with the use of an optimized CT protocol and remote treatment planning. This approach has the potential to improve the quality and safety of emergent after-hours RT by better approximating the normal process of care.
Indexed as
Identifiers
What OpenQuestion holds
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.