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Updated: Dec 25, 2025

Laryngeal Mask Airway LMA Placement in a Neonatal Patient Simulator Using a Non-Inflatable Supraglottic Airway SGA
Published on: July 14, 2023
Validity of laryngomalacia classification systems: A multi-institutional agreement study
Shanmugappiriya Sivarajah1, Andre Isaac1, Shelaina Anderson2
1Division of Otolaryngology Head & Neck Surgery, Division of Pediatric Surgery, Department of Surgery, University of Alberta, Edmonton, Alberta, Canada.
Objectives:
To determine the agreement of paediatric otolaryngologists on classifying laryngomalacia (LM).
Design:
Intra- and interobserver agreement study of two classification systems.
Setting:
Three tertiary referral paediatric centres.
Participants:
Three paediatric otolaryngologists, who were blinded to any clinical details, interpreted the videos of children diagnosed with LM using the Holinger and Olney classifications independently. They rated the videos twice with a washout period of at least 2 weeks.
The Main Outcome Measures:
Inter- and intra-observer agreement measured by overall Fleiss kappa and unweighted Cohen's kappa coefficients. The secondary outcome measures were inter- and intra-observer agreement on the individual anatomical subunits of the supraglottis affected by LM, characterised by the subcategories of the classifications.
Results:
Video records of infants and children <18 years who had an endoscopic diagnosis of LM from 2012 to 2017 were retrospectively chosen for inclusion (n = 73). The overall Fleiss kappa coefficient was 0.25 (95% CI 0.18-0.32) amongst the raters using the Holinger classification and 0.31 (95% CI 0.21-0.42) for the Olney classification. Intra-observer agreement using the Holinger classification was 0.30 (95% CI 0.18-0.42), 0.62 (95% CI 0.23-0.85) and 0.84 (95% CI 0.75-0.94], whilst the Olney classification yielded values of 0.41 (95% CI 0.26-0.56), 0.51 (95% CI 0.29-0.63) and 0.63 (95% CI 0.48-0.78).
Conclusions:
The agreement on types of LM between expert observers is modest using the Holinger and Olney classifications. This has significant implications for accurately diagnosing LM and exposes potential obstacles against credible pooling of data and extrapolation of information.
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