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Interobserver Agreement in the Assessment of Clinical Findings in Children with Headaches

Overview
Journal J Pediatr
Specialty Pediatrics
Date 2020 May 25
PMID 32446483
Citations 2
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Abstract

Objective: To determine the interobserver agreement of history and physical examination findings in children undergoing evaluation in the emergency department (ED) for headaches.

Study Design: We conducted a prospective, cross-sectional study of children aged 2-17 years evaluated at 3 tertiary-care pediatric EDs for non-traumatic headaches. Two clinicians independently completed a standardized assessment of each child and documented the presence or absence of history and physical examination variables. Unweighted κ statistics were determined for 68 history and 24 physical examination variables.

Results: We analyzed 191 paired observations; median age was 12 years, with 19 (9.9%) children younger than 7 years. Interrater reliability was at least moderate (κ ≥ 0.41) for 41 (60.3%) patient history variables. Eleven (61.1%) of 18 physical examination variables for which κ statistics could be calculated had a κ that was at least moderate.

Conclusions: A substantial number of history and physical examination findings demonstrated at least moderate κ statistic values when assessed in children with headaches in the ED. These variables may be generalizable across different types of clinicians for evaluation of children with headaches. If also found to predict the presence or absence of emergent intracranial abnormalities, the more reliable clinical findings may be helpful in the development of clinical prediction rules or risk stratification models that could be used across settings for children with headaches.

Citing Articles

Stratification of risk for emergent intracranial abnormalities in children with headaches: a Pediatric Emergency Care Applied Research Network (PECARN) study protocol.

Tsze D, Kuppermann N, Casper T, Barney B, Richer L, Liberman D BMJ Open. 2023; 13(11):e079040.

PMID: 37993148 PMC: 10668138. DOI: 10.1136/bmjopen-2023-079040.


Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study.

Cowan R, Rapoport A, Blythe J, Rothrock J, Knievel K, Peretz A Headache. 2022; 62(7):870-882.

PMID: 35657603 PMC: 9378575. DOI: 10.1111/head.14324.

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