[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127817-en":3,"doc-seo-127817-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127817,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery - Original Investigation","Lumber disc herniation surgery can substantially reduce pain and disability, yet a significant subgroup experiences limited benefit, creating a need for reliable outcome prediction. This prospective, multicenter, registry-based prognostic study develops and internally externally validates machine learning models for disability and pain at 12 months after surgery. Using Norwegian Registry for Spine Surgery data from 2007–2021, models were evaluated for discrimination and calibration across five geographic regions.","Original Investigation | Surgery  \nMachine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery  \nBjørnar Berg, PhD; Martin A. Gorosito, MSc; Olaf Fjeld, PhD; Hårek Haugerud, PhD; Kjersti Storheim, PhD; Tore K. Solberg, PhD; Margreth Grotle, PhD  \n\n| Abstract\u003Cbr>IMPORTANCE Lumber disc herniation surgery can reduce pain and disability. However, a sizable minority of individuals experience minimal benefit, necessitating the development of accurate prediction models.\u003Cbr>\u003Cbr>OBJECTIVE To develop and validate prediction models for disability and pain 12 months after lumbar disc herniation surgery.\u003Cbr>DESIGN, SETTING, AND PARTICIPANTS A prospective, multicenter, registry-based prognostic study was conducted on a cohort of individuals undergoing lumbar disc herniation surgery from January 1, 2007, to May 31, 2021 . Patients in the Norwegian Registry for Spine Surgery from all public and private hospitals in Norway performing spine surgery were included. Data analysis was performed from January to June 2023 .\u003Cbr>EXPOSURES Microdiscectomy or open discectomy.\u003Cbr>MAIN OUTCOMESAND MEASURES Treatment success at 12 months, defined as improvement in Oswestry Disability Index (ODI) of 22 points or more; Numeric Rating Scale (NRS) back pain improvement of 2 or more points, and NRS leg pain improvement of 4 or more points. Machine learning models were trained for model development and internal-external cross-validation applied over geographic regions to validate the models. Model performance was assessed through discrimination (C statistic) and calibration (slope and intercept) .\u003Cbr>\u003Cbr>RESULTS Analysis included 22707 surgical cases (21161 patients) (ODI model) (mean [SD] age, 47.0 [14.0] years; 12952 [57.0%] males). Treatment nonsuccess was experienced by 33%(ODI), 27%(NRS back pain), and 31%(NRS leg pain) of the patients. In internal-external cross-validation, the selected machine learning models showed consistent discrimination and calibration across all 5 regions. The C statistic ranged from 0.81 to 0.84 (pooled random-effects meta-analysis estimate, 0.82; 95% CI, 0.81-0.84) for the ODI model. Calibration slopes (point estimates, 0.94-1.03; pooled estimate, 0.99; 95% CI, 0.93-1.06) and calibration intercepts (point estimates, −0.05 to 0.11; pooled estimate, 0.01; 95% CI, −0.07 to 0.10) were also consistent across regions. For NRS back pain, the C statistic ranged from 0.75 to 0.80 (pooled estimate, 0.77; 95% CI, 0.75-0.79); for NRS leg pain, the C statistic ranged from 0.74 to 0.77 (pooled estimate, 0.75; 95% CI, 0.74-0.76) . Only minor heterogeneity was found in calibration slopes and intercepts.\u003Cbr>\u003Cbr>CONCLUSION The findings ofthis study suggest that the models developed can inform patients and clinicians about individual prognosis and aid in surgical decision-making.\u003Cbr>JAMA Network Open. 2024;7(2):e2355024. doi:10.1001/jamanetworkopen.2023.55024\u003Cbr> Open Access. This is an open access article distributed under the terms of the CC-BY License. | \u003Cbr>Key Points\u003Cbr>Question Can machine learning models\u003Cbr>accurately predict patient disability and pain following lumbar disc herniation surgery?\u003Cbr>Findings In this prognostic study including 22707 patients, machine learning models were developed and validated in large-scale, nationally representative data for treatment success or nonsuccess in disability and pain 12 months after lumbar disc herniation surgery. The models showed good discrimination and calibration.\u003Cbr>Meaning The findings ofthis study suggest that algorithms can inform about individual prognosis and aid in surgical decision-making to ultimately reduce ineffective and costly spine care.\u003Cbr>+ Supplemental content\u003Cbr>Author affiliations and article information are listed at the end of this article. |\n| --- | --- |\n\nJAMA Network Open. 2024;7(2):e2355024. doi:10.1001/jamanetworkopen.2023.55024 February 7, 2024 1/14  \nDownloaded [from jamanetwork.com](from jamanetwork.com) by UiT The Arctic Univ","cbCaioQrbrDnEZGB","https://ap.wps.com/l/cbCaioQrbrDnEZGB","pdf",1518010,1,14,"English","en",105,"# Abstract\n## Importance\n## Objective\n## Design, setting, and participants\n## Exposures\n## Main outcomes and measures\n## Results\n## Conclusion\n# Introduction\n## Rationale for prediction models\n## Limitations of prior studies\n## Role of national spine registries","[{\"question\":\"What outcome predictions do the machine learning models target in this study?\",\"answer\":\"The models predict treatment success at 12 months, defined by improvement in the Oswestry Disability Index and improvements in Numeric Rating Scale back pain and leg pain.\"},{\"question\":\"How was the study data collected and validated?\",\"answer\":\"It used a prospective, multicenter registry-based cohort from the Norwegian Registry for Spine Surgery, with internal-external cross-validation across five geographic regions.\"},{\"question\":\"How well did the models perform across regions?\",\"answer\":\"They showed consistent discrimination and calibration across all regions, with C-statistics around 0.82 for disability, 0.77 for back pain, and 0.75 for leg pain.\"}]","Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery - Original Investigation | PDF",1785942037,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-models-for-predicting-disability-and-pain-following-lumbar-disc-herniation-surgery-original-investigation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-models-for-predicting-disability-and-pain-following-lumbar-disc-herniation-surgery-original-investigation/127817/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What outcome predictions do the machine learning models target in this study?","Question",{"text":76,"@type":77},"The models predict treatment success at 12 months, defined by improvement in the Oswestry Disability Index and improvements in Numeric Rating Scale back pain and leg pain.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the study data collected and validated?",{"text":81,"@type":77},"It used a prospective, multicenter registry-based cohort from the Norwegian Registry for Spine Surgery, with internal-external cross-validation across five geographic regions.",{"name":83,"@type":74,"acceptedAnswer":84},"How well did the models perform across regions?",{"text":85,"@type":77},"They showed consistent discrimination and calibration across all regions, with C-statistics around 0.82 for disability, 0.77 for back pain, and 0.75 for leg pain.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]