A Brief History of Otolaryngology in the United States From 1847 to 1947

Fenton, Ralph A.

doi:10.1001/archotol.1947.00690020162003

Cited by 5 publications

(3 citation statements)

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“…Otolaryngology-head and neck surgery (OHNS) is the oldest medical specialty in the United States with a rich academic history. 1,2 Through its long history, research has catalyzed advancements in the way otolaryngologists examine and treat their patients. As the volume of research publications in the field of otolaryngology has increased, so has the need to qualify articles through bibliometric analyses to identify the most important and impactful work in the field.…”

Section: Introductionmentioning

confidence: 99%

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Disruption Index in Otolaryngology: Uncovering a Bibliometric History of a Rapidly Evolving Field

et al. 2023

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ObjectivesAs the volume of research publications in the field of otolaryngology has increased, so has the need to qualify articles through bibliometric analyses to identify the most important and impactful work in the field. Herein, we aim to identify the 100 most disruptive articles in ENT over a 60‐year period and examine how disruption index (DI) compares with other bibliometrics in identifying impactful works in the field.MethodsIn this cross‐sectional bibliometric analysis, articles published between 1954 and 2014 in commonly referenced otolaryngology‐head and neck surgery (OHNS) journals were queried in PubMed. Publications were characterized by DI, journal, subspecialty discipline, and status as an impactful article in the field as determined by other bibliometrics such as citation count, the “Sleeping Beauty Index,” and those derived by the modified Delphi process.ResultsOf the 122,094 articles queried, 67,561 (55.3%) had available citation count as well as disruption score data, meeting inclusion criteria. The most represented subspecialty disciplines within the top 100 most disruptive articles were Otology/Neurotology (28%), General (Comprehensive) (27%), Head and Neck Surgery (12%), and Laryngology (11%). Fifty percent of articles identified as Sleeping Beauties and impactful via modified Delphi approach had scores in the top 86th percentile.ConclusionDI in otolaryngology can be appreciated as an added dimension to existing indices and can unearth seminal research, which serve as early foundations of evidence‐based management in the field of OHNS today.Level of EvidenceNA Laryngoscope, 2023

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Section: Introductionmentioning

confidence: 99%

“…Otolaryngology‐head and neck surgery (OHNS) is the oldest medical specialty in the United States with a rich academic history 1,2 . Through its long history, research has catalyzed advancements in the way otolaryngologists examine and treat their patients.…”

Section: Introductionmentioning

confidence: 99%

Disruption Index in Otolaryngology: Uncovering a Bibliometric History of a Rapidly Evolving Field

et al. 2023

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“…Otolaryngology–head and neck surgery is the oldest subspecialization in medicine, and harbors unique opportunities for where modern machine‐learning technologies show potential . Amongst the earliest reports of machine learning applied to clinical topics within otolaryngology–head and neck surgery include automatic recognition of auditory brainstem response waveforms, genomic prediction of oral squamous cell carcinoma, and acoustic voice feature classification .…”

Section: Introductionmentioning

confidence: 99%

A contemporary review of machine learning in otolaryngology–head and neck surgery

et al. 2019

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One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a substantial amount of interest in machine-learning analytic methods. There has been a drastic increase in the otolaryngology literature volume describing novel applications of machine learning within the past 5 years. In this timely contemporary review, we provide an overview of popular machine-learning techniques, and review recent machine-learning applications in otolaryngology-head and neck surgery including neurotology, head and neck oncology, laryngology, and rhinology. Investigators have realized significant success in validated models with model sensitivities and specificities approaching 100%. Challenges remain in the implementation of machine-learning algorithms. This may be in part the unfamiliarity of these techniques to clinician leaders on the front lines of patient care. Spreading awareness and confidence in machine learning will follow with further validation and proof-of-value analyses that demonstrate model performance superiority over established methods. We are poised to see a greater influx of machine-learning applications to clinical problems in otolaryngology-head and neck surgery, and it is prudent for providers to understand the potential benefits and limitations of these technologies.

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A review of the available literature on the larynx and laryngeal surgery for 1947

Orton

1948

The Laryngoscope

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A Brief History of Otolaryngology in the United States From 1847 to 1947

Cited by 5 publications

References 0 publications

Disruption Index in Otolaryngology: Uncovering a Bibliometric History of a Rapidly Evolving Field

Disruption Index in Otolaryngology: Uncovering a Bibliometric History of a Rapidly Evolving Field

A contemporary review of machine learning in otolaryngology–head and neck surgery

A review of the available literature on the larynx and laryngeal surgery for 1947

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