{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T10:00:33Z","timestamp":1790589633352,"version":"4.1.0"},"reference-count":44,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Global Spine Journal"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Systematic review and meta-analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Direct head-to-head comparison of machine learning models aiming to predict outcomes in Anterior Cervical Discectomy and Fusion (ACDF) is necessary because existing studies typically evaluate algorithms in isolation, using heterogeneous datasets, features, and performance metrics, which limits interpretability and prevents meaningful comparison of predictive performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a systematic review and meta-analysis according to PRISMA guidelines, searching PubMed, Embase, Web of Science and Cochrane through December 2024. We identified 26 studies (n = 443\u00a0445 patients) that developed ML models for ACDF outcomes. Algorithms were categorized into five classes by taxonomy: Logistic regression, tree-based, boosting ensembles, kernel methods and NNs. Pooled ML models\u2019 AUCs and accuracy were extracted and estimated via random-effects inverse-variance model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Overall discrimination ranged from 0.59 for major complications to 0.81 for adjacent-level disease. Logistic regression led in predicting unfavorable discharge (AUC 0.76), readmission\/reintervention (0.68) and cost of care (0.83). Boosting ensembles excelled in predicting thromboembolic events (AUC 0.74; 0.68-0.80;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.0001). Neural networks achieved the highest discrimination for opioid prescription (AUC 0.80; 0.75-0.85;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.02) and adjacent-level disease (AUC 0.81; 0.72-0.91;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.01). Kernel methods delivered an exceptional AUC of 0.97 (0.96-0.97) for adjacent-level fusion but underperformed for other outcomes (AUC 0.43-0.49). Decision-tree and mixed-ensemble approaches demonstrated intermediate performance for various outcomes (AUC range 0.54-0.75).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Logistic regression and gradient-boosting models offer robust, generalizable discrimination across diverse ACDF outcomes. Neural networks and kernel methods showed endpoint-specific strengths. These data support prospective validation and rapid integration of ML-driven risk calculators into perioperative workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/21925682261416504","type":"journal-article","created":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T01:59:06Z","timestamp":1769219946000},"page":"3585-3596","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Performance of Machine Learning Models in Predicting Outcomes After ACDF: A Systematic Review and Meta-Analysis of 443\u00a0000 Patients"],"prefix":"10.1177","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0298-1052","authenticated-orcid":false,"given":"Lucas P.","family":"Mitre","sequence":"first","affiliation":[{"name":"UT Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R\u00f4mulo S.","family":"Sanglard","sequence":"additional","affiliation":[{"name":"Federal University of Rio de Janeiro"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6933-0745","authenticated-orcid":false,"given":"Ethan D. 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