ReviewEmergency radiology2020
Mass casualty incidents-are you ready? A major incident planning template for diagnostic radiology.
Review in Emergency radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Ultrasonography in disaster medicine: a narrative review.Insights into imaging · 2026Article
- Automatic Personal Identification Using a Single MRI Slice.Bioengineering (Basel, Switzerland) · 2026Article
- The pager explosions: lessons learnt from a hybrid-warfare mass casualty incident.Emergency medicine journal : EMJ · 2026Article
- Automated body weight measurement using computed tomography.Scientific reports · 2025Article
- Computer vision-based personal identification using 2D maximum intensity projection CT images.European radiology · 2025Article
- Automatic personal identification using a single CT image.European radiology · 2025Article
- Coronavirus Outbreak: Is Radiology Ready? Mass Casualty Incident Planning.Journal of the American College of Radiology : JACR · 2020Review
- A Guide to Mass Casualty Incidents for Radiology Residents: Strategies, Ethics, Directions.Current problems in diagnostic radiologyReview
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mass casualty incidents (MCIs) create a large number of casualties in a short period of time. Diagnostic radiology plays an important role in major incident responses but is often underrepresented during major incident planning (MIP) and simulation. Surveys suggest radiologists are unfamiliar with their role during an MCI. We aimed to identify key topics for radiology MIP, familiarize radiologists with their role during an MCI and identify areas for future research. The terms "radiology" and "mass casualty incident" were entered into the advanced search builder on PubMed. Abstracts from this primary search were reviewed and papers selected for inclusion. Additional studies of interest were identified upon review of reference sections of relevant articles and from the related article tab on PubMed. MCI and trauma guidelines were reviewed. Key factors that caused issues during prior MCIs were identified including staff alert mechanisms, patient identification strategies, patient tracking, scan ordering and result communication. Limitations of local imaging resources and capacity should be identified and inform plans for the utilization of diagnostic radiology in the MCI setting. Simulation can help identify areas for improvement and familiarize staff with their roles. Further development of reliable MCI alert technology and patient identification strategies are needed as well as prospective validation of trauma CT selection criteria to identify patients who will benefit most from CT. Radiology should take part in MIP to address key issues encountered during prior MCIs and in MCI simulation to optimize major incident response.
Indexed as
Identifiers
32060659What 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.