Literature DB >> 23588975

Overlooked extremity fractures in the emergency department.

Erhan Er1, Pınar H Kara, Orhan Oyar, Erden E Ünlüer.   

Abstract

BACKGROUND: The purpose of the study was to analyze the accuracy of interpretation of extremity traumas by emergency physicians (EP) to determine the most difficult areas for interpretation in comparison to official radiology reports of direct X-ray.
METHODS: Radiologist reports and EP reports of direct X-rays from isolated extremity trauma patients were retrospectively compared from 01.05.2011 to 31.05.2011. A total of 181 fractures in 608 cases were confirmed.
RESULTS: The locations of the misinterpreted fractures were ankle and foot (51.4%), wrist and hand (32.4%), elbow and forearm (5.4%), shoulder and upper arm (5.4%), hip and thigh (2.7%), and knee and leg (2.7%). The diagnostic accuracy of the EPs and radiologists were not significantly different (kappa=0.856, p=0.001).
CONCLUSION: Knowledge about the types of fractures that are most commonly missed facilitates a specifically directed educational effort.

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Mesh:

Year:  2013        PMID: 23588975     DOI: 10.5505/tjtes.2013.08555

Source DB:  PubMed          Journal:  Ulus Travma Acil Cerrahi Derg


  4 in total

1.  How Much are Emergency Medicine Specialists' Decisions Reliable in the Diagnosis and Treatment of Pediatric Fractures?

Authors:  Mohsen Mardani-Kivi; Behzad Zohrevandi; Khashayar Saheb-Ekhtiari; Keyvan Hashemi-Motlagh
Journal:  Arch Bone Jt Surg       Date:  2016-01

2.  A pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX) for machine learning.

Authors:  Eszter Nagy; Michael Janisch; Franko Hržić; Erich Sorantin; Sebastian Tschauner
Journal:  Sci Data       Date:  2022-05-20       Impact factor: 8.501

3.  Building Emergency Medicine Trainee Competency in Pediatric Musculoskeletal Radiograph Interpretation: A Multicenter Prospective Cohort Study.

Authors:  Michelle Sin Lee; Martin Pusic; Benoit Carrière; Andrew Dixon; Jennifer Stimec; Kathy Boutis
Journal:  AEM Educ Train       Date:  2019-03-12

4.  Deep learning for accurately recognizing common causes of shoulder pain on radiographs.

Authors:  Nils F Grauhan; Stefan M Niehues; Robert A Gaudin; Sarah Keller; Janis L Vahldiek; Lisa C Adams; Keno K Bressem
Journal:  Skeletal Radiol       Date:  2021-02-20       Impact factor: 2.199

  4 in total

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