{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T19:07:13Z","timestamp":1788894433382,"version":"build-2803163510"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Spine Deform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Spine Deformity is the official journal of the Scoliosis Research Society (SRS) and the leading publication dedicated to scoliosis, deformity, and surgical correction. We applied BERTopic, a transformer-based machine learning topic-modeling framework, to characterize the evolution of research priorities in Spine Deformity from 2013 to 2026.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      All English-language abstracts published in Spine Deformity from January 2013 through May 2026 (\n                      <jats:italic>n<\/jats:italic>\n                      = 1851 after quality filtering) were embedded using PubMedBERT, and then clustered with uniform manifold approximation and projection plus hierarchical density-based spatial clustering of applications with noise, and annotated via class-based term frequency-inverse document frequency. Temporal trends were tested with ordinary least-squares regression and Benjamini\u2013Hochberg false discovery rate correction (\n                      <jats:italic>\u03b1<\/jats:italic>\n                      = 0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>BERTopic identified 29 distinct research topics (noise = 18.4%). Six topics were emerging: postoperative posterior spinal fusion (PSF) outcomes and upper instrumented vertebra (UIV) selection; database studies of length-of-stay (LOS) and adverse events; frailty and perioperative risk; PSF in cerebral palsy and neuromuscular scoliosis; online patient education using large language models (LLMs); and socioeconomic disparities in deformity care. Two topics were cold: sagittal alignment and spinopelvic parameters in AIS, and biomechanical pedicle screw-rod testing. Although no standalone machine learning (ML) cluster emerged, ML and artificial intelligence (AI) methods appeared in 30 abstracts (1.6%), concentrated in radiographic measurement, risk prediction, and LLM-based patient education.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Transformer-based topic modeling provides a data-driven map of Spine Deformity\u2019s first decade, revealing rapid growth in PSF outcomes research, frailty-aware perioperative care, registry-based outcomes analysis, and AI\/LLM applications, alongside maturation of historical sagittal alignment and biomechanical research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s43390-026-01534-4","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T18:25:47Z","timestamp":1788891947000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research priorities in Spine Deformity: a machine learning-based topic analysis of the journal\u2019s first decade (2013\u20132026)"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8611-6711","authenticated-orcid":false,"given":"Rohan A.","family":"Phadke","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samer G.","family":"Salman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan J.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"issue":"10","key":"1534_CR1","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.4187\/respcare.09541379","volume":"54","author":"RA Callcut","year":"2009","unstructured":"Callcut RA, Branson RD (2009) How to read a review paper. 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