VOTE Scores Research Articles

PEPT: Expert Finding Meets Personalized Pre-training

Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. Recently, the application of Pre-trained Language Models (PLMs) have gained significant attraction due to their impressive capability to comprehend textual data, and are widespread used across various domains. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design: 1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest; 2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superiority of our approach over competitive baseline methods. 1

Phenotypic differences in airway obstruction as determined by VOTE score

Phenotypic differences in airway obstruction as determined by VOTE score

The Prevalence of Sentinel Central Events (SCent) During Drug-Induced Sleep Endoscopy.

With the recent addition of airflow and respiratory effort channels, our group has observed central and mixed apnea events during drug-induced sleep endoscopy (DISE). We measured the frequency and timing of sentinel central and/or mixed events (SCents), as well as assessed for differences in velum, oropharynx, tongue, and epiglottis (VOTE) classification compared to obstructive events. Prospective single-cohort study. Tertiary Care Academic Medical Center. Patients underwent DISE between June 2020 and November 2022. Nasal airflow, thoracoabdominal effort belt signals, and videoendoscopy were simultaneously captured. Demographics, sleep study, and DISE data were compared among patients with and without SCents using Student's T tests or χ2 tests. On average, the cohort (n = 103) was middle-aged (53.5 ± 12.1 years), overweight (body mass index of 29.7 ± 5.3 kg/m2), and had severe obstructive sleep apnea (apnea-hypopnea index of 30.7 ± 18.7 events/h). Forty-seven patients (46%) were found to have at least 1 SCent. Among those with SCent, 45 (95.7%) transitioned to obstructive pathology after an average of 7.91 ± 2.74minutes, with at least 95% of patients expected to do so within 12.57minutes. Twenty-nine out of 47 patients (61.2% [95% confidence interval: 46.4.9%, 75.5%]) with SCent had meaningful differences between central/mixed and obstructive VOTE scores. Central events were present in almost half of our cohort. At least 95% of patients were expected to transition to obstructive events within 12 to 13minutes of propofol initiation. In addition, over half of patients demonstrate significantly different VOTE scores between central and obstructive events. These factors should raise awareness of central events and scoring passive apneas during DISE and consider delaying VOTE scoring.

Influence node analysis based on neighborhood influence vote rank method in social network

Social Networks are used for various purposes like advertising, product launches, sentiment analysis, opinion mining, and event detection etc. Terrorist targets social network users to spread the terrorism. Influence analysis is used in social networks to find the influence of users and the impact of the messages, mainly for advertising. In this research, the Neighborhood Influence – Vote Rank (NI-VR) method is proposed to analyze the terrorism and social network datasets temporally to find the influence node in Social networks. The Global Terrorism Dataset (GTD) was used to analyze the terrorism activity and temporal analysis on Social Network data to find the influence node. The Neighborhood node influence is measured and considered in the Social Network data to effectively find the influence node. The nodes’ vote score and vote ability were measured to rank the nodes based on influence. The neighborhood influence is measured to update the vote score and vote ability based on influence value. The neighborhood influence method is applied to rank the node has the advantage of analyzing the probability of affected nodes and recover nodes that help to effectively find the influence nodes. The outcomes illustrate that the proposed NI-VR achieved a maximum spread influence of 843 and the existing Greedy method has a higher spread influence of 840 in influence node analysis.

Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease’s underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ − 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.

Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experiment

Society often relies on social algorithms that adapt to human behavior. Yet scientists struggle to generalize the combined behavior of mutually-adapting humans and algorithms. This scientific challenge is a governance problem when algorithms amplify human responses to falsehoods. Could attempts to influence humans have second-order effects on algorithms? Using a large-scale field experiment, I test if influencing readers to fact-check unreliable sources causes news aggregation algorithms to promote or lessen the visibility of those sources. Interventions encouraged readers to fact-check articles or fact-check and provide votes to the algorithm. Across 1104 discussions, these encouragements increased human fact-checking and reduced vote scores on average. The fact-checking condition also caused the algorithm to reduce the promotion of articles over time by as much as −25 rank positions on average, enough to remove an article from the front page. Overall, this study offers a path for the science of human-algorithm behavior by experimentally demonstrating how influencing collective human behavior can also influence algorithm behavior.

Is electoral intraparty competition more intense in urban areas?

This article investigates whether intraparty competition at the election stage – measured by the distribution of preference votes over candidates running under the same party label – is more intense in urban areas. Earlier research on preferential-list PR systems shows that urban voters are less inclined to cast preference votes. Yet we do not know how these differences at the individual voter level translate to the degree of intraparty competition at the aggregate list level. We hypothesize that urban areas provide a more open electoral market and lead to lower levels of vote concentration on party lists. We leverage a novel dataset on preference voting in three Belgian elections by aggregating 461,049 preference vote scores for candidates-in-cantons to a dataset of intraparty competition scores for 3214 lists-in-cantons. The hierarchical models show that intra-party competition is impacted by the interactive effects of both the urban character of electoral competition and the presence of prominent office-holders. While rural contexts lead to greater vote concentration in presence of a larger number of office-holders, urban contexts often result in lower vote concentration. Our study provides novel insights into the contextual determinants of intraparty competition and personalization.

Madhubani Art Classification using transfer learning with deep feature fusion and decision fusion based techniques

Some traditional Indian art forms enjoy widespread popularity across the world. One of the most prominent among these is the Madhubani style. This art form’s rich heritage and beauty enthrall the connoisseurs and continue to inspire new designs catering to the changing tastes of prevalent fashion. Preservation of these traditional art forms is the need of the hour. Modern technological advances can be utilized with great advantage for this purpose. Since a database of Madhubani art forms was hitherto unavailable, an attempt is made in this work to create one from scratch. Five different classes of Madhubani art, i.e., Bharni, Godna, Kachni, Kohbar, and Tantrik, are identified, and the collected images are annotated with these classes. Classification of the art images is attempted using the handcrafted Local Binary Pattern (LBP) texture descriptors and state-of-the-art Convolutional Neural Networks (CNNs). The Transfer Learning approach with CNNs is employed to classify the designs. An attempt is made to obtain a better classification accuracy than the one provided by standard CNNs. Towards this end, the current work proposes a fusion of features extracted from several deep CNNs, decision fusion-based classification based on averaging prediction score (FAVG), and maximum vote score (FMAX). The proposed method’s performance is tested on our Madhubani art dataset and compared against several standard pre-trained CNNs available in the literature. The proposed approaches provide significantly higher classification accuracy for Madhubani art patterns, with decision fusion based on averaging prediction score (FAVG) approach being the best. The maximum accuracy, specificity, and error rate scores are 98.82%, 99.72%, and 1.18%, respectively. This is the first such attempt, and the excellent results motivate further work to develop content-based image retrieval tools and evolutionary design-based tools for automating the development of new designs. These endeavors are expected to go a long way in preserving precious art heritage and fostering its rapid growth in the world market. The dataset will be made publically available for further experimentation.

Use of VOTE score in predicting outcomes in pediatric obstructive sleep apnea

Obstructive Sleep Apnea (OSA) affects 1-4% of the pediatric population in the U.S. Drug-Induced Sleep Endoscopy (DISE) is widely used to localize the level(s) of obstruction. The VOTE classification system is used to grade obstructions found at the velum, oropharynx, base of tongue, and epiglottis and has been validated in adults. This study aims to determine if the VOTE score has any predictive value in pediatric OSA postoperative outcomes. A retrospective chart review of 129 patients from January 7, 2016 to 05/30/2020 was performed. Included patients were between the ages of 2 and 17, undergoing DISE, and if they had preoperative and postoperative polysomnography (PSG) data. Excluded patients did not meet one of the above or had other comorbidities contributing to their sleep apnea. 53 patients were included. Pearson's Correlation tests compared intraoperative VOTE score to postoperative BMI, AHI, and O2 nadir and their relationships. We found a weakly positive correlation between the VOTE and postoperative AHI with a coefficient of 0.35 and a p-value of 0.01. We found a relationship between postoperative O2 nadir and AHI, with a coefficient of -0.627 and a p-value <0.0001. Finally, a paired, two-tailed t-test compared the mean change between preoperative and postoperative BMIs (+1.6), oAHIs (-23.5), and O2 nadirs (+14), all with p-value <0.0001. We demonstrated a correlation between VOTE and improved postoperative AHI and a relationship between improved postoperative AHI and postoperative O2 nadir. The validity of VOTE may be proven with larger sample size. Alternatively, a different scoring system may be required for pediatric OSA.

Broken External Links on Stack Overflow

Stack Overflow hosts valuable programming-related knowledge with 11,926,354 links that reference to the third-party websites. The links that reference to the resources hosted outside the Stack Overflow websites extend the Stack Overflow knowledge base substantially. However, with the rapid development of programming-related knowledge, many resources hosted on the Internet are not available anymore. Based on our analysis of the Stack Overflow data that was released on Jun. 2, 2019, 14.2% of the links on Stack Overflow are broken links. The broken links on Stack Overflow can obstruct viewers from obtaining desired programming-related knowledge, and potentially damage the reputation of the Stack Overflow as viewers might regard the posts with broken links as obsolete. In this paper, we characterize the broken links on Stack Overflow. 65% of the broken links in our sampled questions are used to show examples, e.g., code examples. 70% of the broken links in our sampled answers are used to provide supporting information, e.g., explaining a certain concept and describing a step to solve a problem. Only 1.67% of the posts with broken links are highlighted as such by viewers in the posts' comments. Only 5.8% of the posts with broken links removed the broken links. Viewers cannot fully rely on the vote scores to detect broken links, as broken links are common across posts with different vote scores. The websites that host resources that can be maintained by their users are referenced by broken links the most on Stack Overflow -- a prominent example of such websites is GitHub. The posts and comments related to the web technologies, i.e., JavaScript, HTML, CSS, and jQuery, are associated with more broken links. Based on our findings, we shed lights for future directions and provide recommendations for practitioners and researchers.

0744 A Comparison of Visual and Physiologic Assessments of Upper Airway Collapse during Drug-Induced Sleep Endoscopy (DISE)

Abstract Introduction The VOTE score, critical closing pressure (Pcrit), and therapeutic CPAP levels are assessments that quantify upper airway collapse by either subjective visual scoring or objective pressure-flow analysis. We hypothesized that there would be an association between collapse severity and physiologic metrics (VOTE versus Pcrit and PAP levels, respectively) acquired during drug-induced sleep endoscopy (DISE). Methods This prospective cohort study evaluated 100 consecutive patients with obstructive sleep apnea (OSA) who underwent DISE with application of nasal positive airway pressure between June 3rd, 2020 and June 11th, 2021. Patients were assigned a VOTE score of 0, 1, or 2 indicating no collapse, partial collapse, or complete collapse at the velum, oropharynx, tongue base, and epiglottis, respectively. We assessed two metrics of pharyngeal collapsibility with progressive increases in nasal pressure: (1) the pressure at which inspiratory airflow commences (critical pressure, Pcrit), and (2) the pressure at which inspiratory flow limitation is abolished (pharyngeal opening pressure, PhOP). Our analysis examined the association between the composite VOTE score [0-8 units] and collapsibility metrics. Results Ninety-one patients met inclusion criteria for VOTE scoring; of these, 87 were included for PhOP analysis and 79 for Pcrit analysis. The cohort was 76% male, mean (SD) age was 54.7 (14.1) years, body-mass index was 29.7 (4.9), and AHI was 30.3 (20.9). Composite VOTE score was positively associated with Pcrit (β = 0.88 ± 0.38 cm H2O per unit, standardized estimate = 0.26, p=0.023). We found no significant association between the composite VOTE score and PhOP (β = 0.57 ± 0.40 cm H2O per unit, standardized estimate = 0.15, p=0.162). Conclusion Our findings suggest that visual and physiological assessments of upper airway collapsibility provide both overlapping and complementary information in characterizing upper airway mechanics. Measures of pharyngeal collapsibility during DISE can be used to model the ultimate impact of therapeutic maneuvers on OSA. Future studies investigating the utility of each assessment both in isolation and in combination for predicting OSA therapy outcomes are indicated. Support (If Any) National Institutes of Health: 1R01HL144859

Diminishing returns of information quality: Untangling the determinants of best answer selection

Online question and answer (Q&A) platforms are ideal live repositories for knowledge sharing and exchange. On Q&A platforms, a seeker may receive one answer to a question, none, or multiple answers. In the case of many answers, it is important to understand which answer will be selected as the best to inform how seekers retrieve knowledge-based solutions to inquiries.This study develops a best answer selection model that integrates the characteristics of the information exchanged as perceived by the knowledge community and those of the individuals exchanging such information (i.e., answers contributors and answers seekers). Using data from StackOverflow.com, we built conditional logit models to examine the determinants of the best answer selection. Results show that contributors' reputation and the quality of the information (measured as the net vote score received by an answer) are important determinants of best answer selection. This research theorizes on and uncovers a non-linear relationship between the net vote score received by an answer (a proxy for information quality) and its likelihood of being selected as the best answer by the seeker. We also find that the seekers' reputation and the contributors’ reputation influence how information quality affects the selection of the best answer.

Can drug-induced sleep endoscopy (DISE) predict compliance with positive airway pressure therapy? A pilot study.

Drug-induced sleep endoscopy (DISE) has been poorly exploredas an examination to assess positive airway pressure (PAP) therapy in patients with obstructive sleep apnea (OSA). The present study aimed to identify by DISE possible characteristics related to low compliance with PAP therapy due to respiratory complaints. Patients using PAP for OSA underwent DISE in two conditions: (1) baseline (without PAP) and (2) PAP (with the same mask and airway pressure used at home). We compared patients reporting low compliance to PAP due to respiratory complaints to those well-adapted to therapy. VOTE classification (assessment of velopharynx, oropharynx, tongue base, and epiglottis)and TOTAL VOTE score (the sum of VOTE scores at each anatomical site) were assessed. ROC curve analyzedthe accuracy of TOTAL VOTE to predict low compliance due to persistent pharyngeal obstruction in both conditions. Of 19 patients enrolled, all presented multilevel pharyngeal obstruction at baseline condition, with no difference between groups at this study point. When PAP was added, the median VOTE value was higher in the epiglottis (P value=0.02) and tended to be higher at the velum and tongue base in the poorly adapted group; TOTAL VOTE score was also significantly increased in patients withlow compliance (P value<0.001). ROC curve demonstrated that patients with TOTAL VOTE scored 2.5 or more during DISE with PAP presented a 4.6-fold higher risk for low compliance with PAP therapy due to pharyngeal obstruction (AUC: 0.88±0.07; P value<0.01; sensitivity: 77%; specificity: 83%). Adding PAP during aDISE examination mayhelp to predict persistent pharyngeal obstruction during PAP therapy.

Emotional consequences and attention rewards: the social effects of ratings on Reddit

ABSTRACT Rating features on social media platforms affect visibility algorithms and act as symbolic markers of evaluation. This paper addresses the social effects of content ratings through a case study of Reddit. Reddit is a social news site on which users in topic-based communities (subreddits) create posts upon which others upvote, downvote, and comment. Vote scores indicate convergence with, and divergence from, community norms. Analysing data from the platform's three most popular subreddits, we ask: How do rating features afford emotional expression and content engagement? Findings from a Variable-Lag Granger Causality model show that for a portion of Reddit users (14.5%), vote scores predict subsequent emotional expression, with upvotes preceding positive sentiments and downvotes preceding negative sentiments. This is the first systematic test of how ratings influence emotional expression on a social media platform. Findings also show that downvoted content receives higher levels of engagement than upvoted content. Together, these findings suggest a paradox in which divergence from community norms, as indicated by vote score patterns, have emotional consequences and attention rewards.

Pulmonary Nodule Classification Using Feature and Ensemble Learning-Based Fusion Techniques

The Pulmonary nodule indicates the presence of lung cancer. The deep convolutional neural networks (DCNNs) have been widely used to classify the pulmonary nodule as benign or malignant. However, an individual learner usually performs unsatisfactorily due to limited response space, incorrect selection of hypothesis space, or falling into local minimums. To investigate these issues, we propose ensemble learners fusion techniques based on averaging of prediction score and maximum vote score (MAX-VOTE). First, the support vector machine (SVM) and AdaBoostM2 machine learning algorithms are trained on the deep features from DCNNs. The results of both classifiers are fused separately based on averaging of the prediction score. Secondly, the feature fusion technique is developed by fusing the feature of three DCNNs (AlexNet, VGG-16 and VGG-19) through predefined rules. After that, the SVM and AdaBoostM2 are trained on fused features independently to build ensemble learners by fusing the multiple DCNN learners. The predictions of all DCNN learners are fused based on the MAX-VOTE. The results show that the ensemble learners based MAX-VOTE technique yields better performance out of twelve single learners for binary class classification of pulmonary nodules. The proposed fusion techniques are also tested for multi-class classification problem. The SVM based feature fusion technique performs better as compared to all the implemented and the state-of-the-art techniques. The achieved maximum accuracy, AUC and specificity scores are 96.89%±0.25, 99.21%±0.10 and 97.70%±0.21, respectively.

Consistency analysis and influencing factors of performing VOTE scores for drug-induced sleep endoscopy

Objective: To investigate the consistency of Velum, Oropharygneal, Tongue base, Epiglottis (VOTE) scores between two surgeons with similar clinical experience in obstructive sleep apnea hypopnea syndrome (OSAHS) patients with different degree of disease, and to analyze the influencing factors leading to the difference in score. Methods: This was a cross-sectional study. 64 preoperative drug-induced sleep endoscopy (DISE) videos of OSAHS patients during December 2014 to July 2018, from Nanfang Hospital, Southern Medical University were analyzed. The VOTE score was assessed single-blind by two similar experienced surgeons, and the Kappa value between the two scorers was calculated by the third researcher. According to the characteristics of the case, Fisher's exact test or chi-square test method was used to further explore the factors that influenced the consistency. Results: Sixty-four patients were divided into four groups according to the severity of the disease, including mild (7 cases), moderate (30 cases), severe(18 cases), and extremely severe (9 cases). The scores evaluated between two researchers were analysed for consistency. For mild patients, the two scorers were completely consistent in the configuration and degree of obstruction in the velum and epiglottis (Kappa=1). There was no agreement on whether obstruction or not, obstructed configuration, obstructed degree of the oropharynx and tongue base, and presence of velum and epiglottis obstruction. For moderate patients, the two scorers had a good consistency in the configuration and degree of the velum (0.61≤Kappa≤0.80), and there was no consistency in the evaluation of the degree of tongue base and epiglottis (P>0.05). The consistency of the remaining obstructed conditions in the four planes was generally or moderate (0.21≤Kappa≤0.60). For patients with severe OSAHS, the two raters were completely consistent in the evaluation of palatopharyngeal and epiglottic planes for the presence of obstruction, but there was no consistency in the degree of obstruction. Although the degree of obstruction in the oropharyngeal plane can be assessed with good consistency, the consistency of whether the plane was blocked or not was generally not high. In the assessment of other obstructive conditions in the four planes of severe patients, the agreement between the two scorers was moderate or generally. For extremely severe patients, the two scorers were completely consistent in the evaluation of the velum obstruction, but there was no consistency in the degree of obstruction of the oropharynx and tongue base, and the obstruction configuration and degree of the epiglottis. The evaluation of other obstructed conditions in the four planes is good or moderate. Among the patients with severe OSAHS, the difference in the assessment of obstruction of the oropharynx was associated with tonsil size (P<0.05). Conclusion: When physicians with similar clinical experience scored VOTE, the consistency of whether the velum and oropharyngeal planes are obstructed is related to the severity of the disease. Better consistency is observed among more severe OSAHS patients. The reason for the poor consistency of the oropharyngeal plane in severe OSAHS patients OSAHS is due to the difference of the tonsils size. For severe OSAHS patients with small tonsils, the assessment of whether the oropharynx is obstructed should be more cautious.

Broken external links on Stack Overflow

Stack Overflow hosts valuable programming-related knowledge with 11,926,354 links that reference to the third-party websites. The links that reference to the resources hosted outside the Stack Overflow websites extend the Stack Overflow knowledge base substantially. However, with the rapid development of programming-related knowledge, many resources hosted on the Internet are not available anymore. Based on our analysis of the Stack Overflow data that was released on Jun. 2, 2019, 14.2% of the links on Stack Overflow are broken links. The broken links on Stack Overflow can obstruct viewers from obtaining desired programming-related knowledge, and potentially damage the reputation of the Stack Overflow as viewers might regard the posts with broken links as obsolete. In this paper, we characterize the broken links on Stack Overflow. 65% of the broken links in our sampled questions are used to show examples, e.g., code examples. 70% of the broken links in our sampled answers are used to provide supporting information, e.g., explaining a certain concept and describing a step to solve a problem. Only 1.67% of the posts with broken links are highlighted as such by viewers in the posts' comments. Only 5.8% of the posts with broken links removed the broken links. Viewers cannot fully rely on the vote scores to detect broken links, as broken links are common across posts with different vote scores. The websites that host resources that can be maintained by their users are referenced by broken links the most on Stack Overflow -- a prominent example of such websites is GitHub. The posts and comments related to the web technologies, i.e., JavaScript, HTML, CSS, and jQuery, are associated with more broken links. Based on our findings, we shed lights for future directions and provide recommendations for practitioners and researchers.

0674 Association Between Lateral Wall Collapse On Drug-induced Sleep Endoscopy And MRI Findings In Hypoglossal Nerve Stimulator Patients

Abstract Introduction Hypoglossal nerve stimulator (HNS) is an effective and safe alternative therapy for obstructive sleep apnea (OSA) in selected patients. Emerging evidence demonstrates that the outcome of HNS is variable, especially for patients with lateral wall collapse on drug-induced sleep endoscopy (DISE). Awake magnetic resonance imaging (MRI) offers detailed visualization of soft tissue. The aim of this study was to determine whether lateral wall collapse on DISE is associated with awake MRI findings in prospective HNS patients. Methods Patients from the ADHERE Registry, an international outcomes study for UAS were used for this study. At baseline, awake, supine MRI scans of each subject’s head and neck region were collected. The distance between the lateral walls was measured at the level of the hard palate, located by the appearance of the posterior nasal spine, using axial T2 turbo spin echo MRI. DISE assessments of the upper airway were recorded using the VOTE classification. All statistical analyses were performed using SPSS IBM 19.0 software program. Kendall’s Tau-b was performed to compare the association between VOTE scoring and MRI findings. Results Twenty-seven patients (N = 3 female, AHI = 28.8±10.5, BMI = 28.8 ±3.8 kg/m2, age = 53±9.9 years) were included in this study. The mean overall VOTE score and lateral wall score was 5.6±1.1 and 0.5±0.5, respectively. The mean lateral wall distance was 18.8±3.2 mm. A significant, inverse association was found between MRI lateral wall measurement and oropharyngeal lateral wall scoring on DISE (T=-.332,p=0.042) but not other anatomic subsites on DISE. Conclusion In our study, greater lateral wall collapse on DISE corresponded to narrower lateral airway distance on MRI. The utility of static imaging modalities such as MRI as patient selection tools for HNS warrants further study. Support Drs. Dedhia and Schwab receive related support for this project from the National Institutes of Health (NHBLI R01HL144859)

Analyzed the related factors of VOTE score for drug-induced sleep endoscopy in patients with obstructive sleep apnea

Objective:To analyze the related factors of VOTE score for drug-induced endoscopy（DISE） in patients with obstructive sleep apnea （OSA）. Method:Fifty-four OSA patients, diagnosed by polysomnograph, underwent surgical treatment from Nov 2014 to Dec 2016 in our hospital. All patients underwent drug induced sleep endoscope, and then the collapse of pharyngeal space was evaluated. We analyzed the related factors with VOTE score. Result:The occlusion rates were significant statistical different in different spaces of 54 OSA patients undergoing DISE（P=0.000, velum 98.15%, oropharynx 81.48%, tongue base 40.47%, and epiglottis 11.11% respectively）. The rateand of tongue base collapsing was related with Mallampatis（P<0.05） and Friedman stage（P<0.05）. The VOTE score was weakly related with Friedman stage（r=0.297, P<0.05）, medium related with BMI（r=0.376, P<0.05）, AHI（r=0.312, P<0.05） and lowest SpO2（r=0.376, P<0.01）. Conclusion:In the VOTE scoring system for DISE, the rate of collapse in tongue base was related with Mallampatis and Friedman stage. The VOTE score was medium related with BMI, AHI and Lowest SpO2, mild related with Friedman stage.

Intra-rater and Inter-rater consistency of drug induced sleep endoscopy

Objective: Drug induced sleep endoscopy (DISE) is a valuable tool which is used in the diagnosis of obstructive sleep apnea (OUA). The aim of this study is to evaluate inter-rater and intra-rater consistency of DISE. Methods: 36 OSA patients with Apnea-hypopne index>5 included in this study. DISE was performed and recorded digitally for all patients, by the first author (OA1). VOTE scores were noted to procedure report in patients’ charts. Video records of DISE were blindly evaluated six months after the last procedure, by observer 1 for the second time (OA2) and by observer 2 (OB) for the first time. DISE was evaluated by using VOTE classification. OA1 and OA2 scores were compared to determine intra-rater reliability and OA2 and OB scores were compared to determine inter- rater reliability. Results: Inter-rater consistency of DISE was poor to good. Highest consistency rate was found in velum at anteroposterior configuration, while the lowest was found in the same level at lateral configuration. Intra-rater consistency of DISE was moderate to excellent. Highest consistency rate was found in epiglottis at lateral configuration, while the lowest was found in oropharynx level. Conclusion: OSA is condition with possible serious complications. DISE is a tool that could change the course of treatment in OSA. The validity of DISE is quite acceptable although a golden standard classification tool could enable us to “speak the exact same language” and will surely increase the diagnostic success of DISE.