{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T18:26:24Z","timestamp":1777659984474,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:00:00Z","timestamp":1728950400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:00:00Z","timestamp":1728950400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Provincial Science and Technology Plan Project Special Fund Transfer Payment Project","award":["2021-YF08-00113-GX"],"award-info":[{"award-number":["2021-YF08-00113-GX"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82302785"],"award-info":[{"award-number":["82302785"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"1.3.5 project for Postdoctoral Foundation of West China Hospital of Sichuan University","award":["2023HXBH080"],"award-info":[{"award-number":["2023HXBH080"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Global Spine Journal"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Machine learning model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a machine learning (ML) model to predict moderate-severe anterior bone loss (ABL) following anterior cervical disc replacement (ACDR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective review of patients undergoing ACDR or Hybrid surgery (HS) at a single center was performed. Patients diagnosed as C3-7 single- or multi-level cervical disc degenerative diseases (CDDD) with more than 2\u00a0years of follow-up and complete pre- and postoperative radiological imaging were included. An ML-based algorithm was developed to predict moderate-severe ABL based on perioperative demographic, clinical, and radiographic parameters. Model performance was evaluated in terms of discrimination and overall performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 339 ACDR segments were included (61.65% female, mean age 45.65 \u00b1 8.03\u00a0years). During a follow-up period of 45.65 \u00b1 8.03\u00a0months, 103 (30.38%) segments developed moderate-severe ABL. The model demonstrated good discrimination and overall performance according to precision (moderate-severe ABL: 0.71 \u00b1 0.07, none-mild ABL: 0.73 \u00b1 0.08), recall (moderate-severe ABL: 0.69 \u00b1 0.08, none-mild ABL: 0.75 \u00b1 0.07), F1-score (moderate-severe ABL: 0.70 \u00b1 0.08, none-mild ABL: 0.74 \u00b1 0.07), and area under the curve (AUC) (0.74 \u00b1 0.10). The most important predictive features were higher height change, higher post-segmental angle, and longer operation time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Utilizing a ML approach, this study successfully identified risk factors and accurately predicted the development of moderate-severe ABL following ACDR, demonstrating robust discrimination and overall performance. By overcoming the limitations of traditional statistical methods, ML can enhance discovery, clinical decision-making, and intraoperative techniques.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/21925682241293712","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:13:26Z","timestamp":1729037606000},"page":"2236-2245","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Using Machine Learning Algorithms to Predict Postoperative Anterior Bone Loss Following Anterior Cervical Disc Replacement"],"prefix":"10.1177","volume":"15","author":[{"given":"Rui","family":"Zong","sequence":"first","affiliation":[{"name":"Sichuan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Can","family":"Guo","sequence":"additional","affiliation":[{"name":"Sichuan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-bo","family":"He","sequence":"additional","affiliation":[{"name":"Sichuan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting-kui","family":"Wu","sequence":"additional","affiliation":[{"name":"Sichuan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5330-4193","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"Sichuan University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,10,15]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"crossref","unstructured":"Singh M Balmaceno-Criss M Anderson G et al. 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