Proliferation and Contributions of National Database Studies in Otolaryngology Literature Published in the United States: 2005-2016

Subbarayan, Rahul S.; Koester, Lindsey; Villwock, Mark R.; Villwock, Jennifer A.

doi:10.1177/0003489418784968

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…Subbarayan et al identified 310 studies published on various national databases between 2005 and 2016 within the otolaryngology literature alone. 21 These database studies are highly advantageous because of their inherent statistical power. 7 However, more often than not, we are confronted with myriad tables of statistics that relate to whatever clinical question is being investigated.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer

Shew

New

Bur

2019

Otolaryngol.--head neck surg.

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Objective To apply a novel methodology with machine learning (ML) to a large national cancer registry to help identify patients who are high risk for delayed adjuvant radiation. Study Design Observational cohort study. Setting National Cancer Database (NCDB). Subjects and Methods A total of 76,573 patients were identified from the NCDB who had invasive head and neck cancer and underwent surgery, followed by radiation. The model was constructed from 80% of the patient data and subsequently evaluated and scored with the remaining 20%. Permutation feature importance analysis was used to understand the weighted model construction. Results A total of 76,573 patients met inclusion and exclusion criteria. Our ML model was able to predict whether patients would start adjuvant therapy beyond 50 days after surgery with an overall accuracy of 64.41% and a precision of 58.5%. The 2 most important variables used to build the model were treating facility and urban versus rural demographics. Conclusion Statistics can provide inferences within an overall system, while ML is a novel methodology that can make predictions. We can identify patients who are “high risk” for delayed radiation using information from >75,000 patient experiences, which has the potential for a direct impact on clinical care. Our inability to achieve greater accuracy is due to limitations of the data captured by the NCDB, and we need to continue to identify new variables that are correlated with delayed radiation therapy. ML will prove to be a valuable clinical tool in years to come, but its utility is limited by available data.

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Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer

Shew

New

Bur

2019

Otolaryngol.--head neck surg.

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“…An area where ML may be of additional value is in the analysis of large clinical data registries. Multi-institutional, national, and international data registries allow higher statistical power and open doors to address previously difficult-to-answer questions [27]. Due to the aforementioned strengths, a model developed using ML would be able to account for intricate interactions among variables [26].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict Treatment in Oropharyngeal Squamous Cell Carcinoma

Karadaghy

Shew

New

et al. 2021

ORL

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Objectives: This study seeks to (1) demonstrate how machine learning (ML) can be used for prediction modeling by predicting the treatment patients with T1–2, N0–N1 oropharyngeal squamous cell carcinoma (OPSCC) receive and (2) assess the impact patient, socioeconomic, regional, and institutional factors have in the treatment of this population. Methods: A retrospective cohort of adults diagnosed with T1–2, N0–N1 OPSCC from 2004 to 2013 was obtained using the National Cancer Database. The data was split into 80/20 distribution for training and testing, respectively. Various ML algorithms were explored for development. Area under the curve (AUC), accuracy, precision, and recall were calculated for the final model. Results: Among the 19,111 patients in the study, the mean (standard deviation) age was 61.3 (10.8) years, 14,034 (73%) were male, and 17,292 (91%) were white. Surgery was the primary treatment in 9,533 (50%) cases and radiation in 9,578 (50%) cases. The model heavily utilized T-stage, primary site, N-stage, grade, and type of treatment facility to predict the primary treatment modality. The final model yielded an AUC of 78% (95% CI, 77–79%), accuracy of 71%, precision of 72%, and recall of 71%. Conclusion: This study created a ML model utilizing clinical variables to predict primary treatment modality for T1–2, N0–N1 OPSCC. This study demonstrates how ML can be used for prediction modeling while also highlighting that tumor and facility realted variables impact the decision making process on a national level.

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“…When diseases encountered in daily practice are evaluated, otolaryngology practice is generally formed of otological and rhinological diseases. [11][12][13][14][15] In the publications from Turkey in the field of otolaryngology, the articles which were most cited were seen to be mostly otology and rhinology. 2,3 The reason for the highest number of pediatric studies was considered as the most widely accepted journal in this study was the International Journal of Pediatric Otorhinolaryngology.…”

Section: Discussionmentioning

confidence: 99%

A 6-Year Analysis of Publications from Turkey in the Field of Otolaryngology in Science Citation Index Journals: Before and After the Change in Criteria for Associate Professorship

Gökgöz¹,

Taşlı²,

Karakoç³

2020

KBB ve BBC Dergisi

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Varying criteria for associate professorship have directed researchers to write more articles and to publish in international journals. Although there have been previous studies containing evaluations of the number of citations, the current study is the first to have evaluated the number of authors and their institutions. Material and Methods: Sixteen journals in the 2018 Science Citation Index in the field of otolaryngology were examined. A record was made of which journal the article was published in, the date, the number of authors, where the authors worked, the subject of the article, and whether or not it was an animal study. Results: A total of 398 original research articles were published in the 16 SCI journals in the field of otolaryngology between 01.01.2014 and 01.09.2019. When the institution of the leading author was examined, there were seen to be more articles accepted from universities (51.25%). When the articles were classified according to subject, the most articles in the field of otolaryngology were seen to be on the subject of pediatric otolaryngology (38.69%). When the number of authors was evaluated according to the year of publication, it was determined to be mean 6.

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Proliferation and Contributions of National Database Studies in Otolaryngology Literature Published in the United States: 2005-2016

Abstract: There was a nearly 10-fold increase in database publications in 2016 compared to 2005. The majority provide descriptive data and outcomes measures. The role of these studies warrants further investigation.

Cited by 12 publications

References 17 publications

Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer

Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer

Machine Learning to Predict Treatment in Oropharyngeal Squamous Cell Carcinoma

A 6-Year Analysis of Publications from Turkey in the Field of Otolaryngology in Science Citation Index Journals: Before and After the Change in Criteria for Associate Professorship

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