ArticleGlobal spine journal2023
Leveraging Artificial Intelligence and Synthetic Data Derivatives for Spine Surgery Research.
Article in Global spine journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Review
- Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods.Health information science and systems · 2025Article
- AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches.Journal of wrist surgery · 2025Article
- A practical guide to the implementation of AI in orthopaedic research-Part 5: Data management.Journal of experimental orthopaedics · 2025Review
- Large Language Model-Based Writing in Published Sports Medicine Research: Uncovering a Growing Influence.Orthopaedic journal of sports medicine · 2025Article
- Synthetic data as an investigative tool in hypertension and renal diseases research.World journal of methodology · 2025Article
- Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.JCO clinical cancer informatics · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
STUDY
designRetrospective cohort study.
objectivesLeveraging electronic health records (EHRs) for spine surgery research is impeded by concerns regarding patient privacy and data ownership. Synthetic data derivatives may help overcome these limitations. This study's objective was to validate the use of synthetic data for spine surgery research.
methodsData came from the EHR from 15 hospitals. Patients that underwent anterior cervical or posterior lumbar fusion (2010-2020) were included. Real data were obtained from the EHR. Synthetic data was generated to simulate the properties of the real data, without maintaining a one-to-one correspondence with real patients. Within each cohort, ability to predict 30-day readmissions and 30-day complications was evaluated using logistic regression and extreme gradient boosting machines (XGBoost).
resultsWe identified 9,072 real and 9,088 synthetic cervical fusion patients. Descriptive characteristics were nearly identical between the 2 datasets. When predicting readmission, models built using real and synthetic data both had c-statistics of .69-.71 using logistic regression and XGBoost. Among 12,111 real and 12,126 synthetic lumbar fusion patients, descriptive characteristics were nearly the same for most variables. Using logistic regression and XGBoost to predict readmission, discrimination was similar with models built using real and synthetic data (c-statistics .66-.69). When predicting complications, models derived using real and synthetic data showed similar discrimination in both cohorts. Despite some differences, the most influential predictors were similar in the real and synthetic datasets.
conclusionSynthetic data replicate most descriptive and predictive properties of real data, and therefore may expand EHR research in spine surgery.
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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.