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Adults undergoing elective ACDF from 2006 to 2020 were analyzed, excluding patients with missing 1-year dysphonia data.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/machine-learning-models-for-predicting-dysphonia-following-anterior-cervical-discectomy-and-fusion-a-swedish-registry-study/290621/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/machine-learning-models-for-predicting-dysphonia-following-anterior-cervical-discectomy-and-fusion-a-swedish-registry-study/290621.png","ImageObject",300,407,{"name":42,"@type":43},"Asher","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-22","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What complication does the study aim to predict after ACDF?","Question",{"text":63,"@type":64},"The study targets persistent dysphonia after anterior cervical discectomy and fusion (ACDF), lasting at least 1 month after surgery.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were participants selected for the Swedish registry analysis?",{"text":68,"@type":64},"All adults who underwent elective ACDF between 2006 and 2020 in the Swespine registry were included, and patients with missing dysphonia data at the 1-year follow-up were excluded.",{"name":70,"@type":61,"acceptedAnswer":71},"Which machine learning model performed best and what does that imply?",{"text":72,"@type":64},"Random forest (RF) achieved the best performance (AUC=0.794). It showed high sensitivity and consistent accuracy, though it had low specificity and positive predictive value.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290621,1789642219,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":55,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Zurich Open Repository and Archive  \nUniversity of Zurich  \nUniversity Library Strickhofstrasse 39  \nCH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch)  \nYear: 2024  \nMachine Learning Models for Predicting Dysphonia Following Anterior Cervical Discectomy and Fusion : A Swedish Registry Study  \nBuwaider, Ali ; El-Hajj, Victor Gabriel ; MacDowall, Anna ; Gerdhem, Paul ; Staartjes, Victor E ; Edström, Erik ;  \nElmi-Terander, Adrian  \nDOI: [https://doi.org/10.1016/j.spinee.2024.10.010](https://doi.org/10.1016/j.spinee.2024.10.010)  \nPosted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-267253](https://doi.org/10.5167/uzh-267253)  \nJournal Article Accepted Version  \nThe following work is licensed under a Creative Commons: Attribution 4.0 International (CC BY 4.0) License.  \nOriginally published at:  \nBuwaider, Ali; El-Hajj, Victor Gabriel; MacDowall, Anna; Gerdhem, Paul; Staartjes, Victor E; Edström, Erik; Elmi-Terander, Adrian (2024) . Machine Learning Models for Predicting Dysphonia Following Anterior Cervical Discectomy and Fusion : A Swedish Registry Study. The Spine Journal:Epub ahead of print.  \nDOI: [https://doi.org/10.1016/j.spinee.2024.10.010](https://doi.org/10.1016/j.spinee.2024.10.010)  \nARTICLE IN PRESS  \nThe Spine Journal 000 (2024) 1􀀕10  \nClinical Study  \nMachine learning models for predicting dysphonia following anterior cervical discectomy and fusion: a Swedish Registry Study  \nAli Buwaidera, 1, Victor Gabriel El-Hajja, 1, Anna MacDowall, MD, PhDb,c, Paul Gerdhem, MD, PhDb,c,d, Victor E Staartjes, MD, PhDe, Erik Edstr€om, MD, PhDa,f,g, Adrian Elmi-Terander, MD, PhDa,b,f,g, *  \na Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden b Department of Surgical Sciences, Uppsala University, Uppsala, Sweden c Department of Orthopaedics and Hand surgery, Uppsala University Hospital, Uppsala, Sweden d Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden e Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience  \nCenter, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland f Capio Spine Center Stockholm, L€owenstr€omska Hospital, Upplands-V€asby, Sweden g Department of Medical Sciences, €Orebro University, €Orebro, Sweden  \nReceived 23 June 2024; revised 27 September 2024; accepted 27 October 2024  \nAbstract BACKGROUND: Dysphonia is one of the more common complications following anterior cervi  \ncal discectomy and fusion (ACDF) . ACDF is the gold standard for treating degenerative cervical spine disorders, and identifying high-risk patients is therefore crucial.  \nPURPOSE: This study aimed to evaluate different machine learning models to predict persistent dysphonia after ACDF.  \nSTUDY DESIGN: A retrospective review of the nationwide Swedish spine registry (Swespine) .  \nPATIENT SAMPLE: All adults in the Swespine registry who underwent elective ACDF between 2006 and 2020 .  \nOUTCOME MEASURES: The primary outcome was self-reported dysphonia lasting at least 1 month after surgery. Predictive performance was assessed using discrimination and calibration metrics.  \nMETHODS: Patients with missing dysphonia data at the 1-year follow-up were excluded. Data preprocessing involved one-hot encoding categorical variables, scaling continuous variables, and imputing missing values. Four machine learning models (logistic regression, random forest (RF), gradient boosting, K-nearest neighbor) were employed. The models were trained and tested using an 80:20 data split and 5-fold cross-validation, with performance metrics guiding the selection of the best model for predicting persistent dysphonia.  \nRESULTS: In total, 2,708 were included in the study. Twelve key predictors were identified. Four machine learning models were tested, with the RF model achieving the best performance (AUC=0.794) . The most significant pred","cbCaierGCQdTTZzY","https://ap.wps.com/l/cbCaierGCQdTTZzY","pdf",1030744,"English","# Abstract\n## Background\n## Purpose\n## Study design\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What complication does the study aim to predict after ACDF?\",\"answer\":\"The study targets persistent dysphonia after anterior cervical discectomy and fusion (ACDF), lasting at least 1 month after surgery.\"},{\"question\":\"How were participants selected for the Swedish registry analysis?\",\"answer\":\"All adults who underwent elective ACDF between 2006 and 2020 in the Swespine registry were included, and patients with missing dysphonia data at the 1-year follow-up were excluded.\"},{\"question\":\"Which machine learning model performed best and what does that imply?\",\"answer\":\"Random forest (RF) achieved the best performance (AUC=0.794). It showed high sensitivity and consistent accuracy, though it had low specificity and positive predictive value.\"}]","Machine Learning Models for Predicting Dysphonia Following Anterior Cervical Discectomy and Fusion - A Swedish Registry Study | PDF",28]