ArticleSpine deformity2026
Research priorities in Spine Deformity: a machine learning-based topic analysis of the journal's first decade (2013-2026).
Article in Spine deformity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
purposeSpine 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.
methodsAll English-language abstracts published in Spine Deformity from January 2013 through May 2026 (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-Hochberg false discovery rate correction (α = 0.05).
resultsBERTopic 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.
conclusionTransformer-based topic modeling provides a data-driven map of Spine Deformity's 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.
Indexed as
Identifiers
42711523What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.