ArticleScientific reports2025
Predicting proximal junctional failure in adult spinal deformity patients using machine learning models based on spinal alignment parameters.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction of proximal junctional pathology after adult spinal deformity surgery: a systematic review and diagnostic test accuracy meta-analysis.Acta neurochirurgica · 2026Pooled it
- Pathogenic mechanisms of adult degenerative scoliosis.Frontiers in cell and developmental biology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proximal junctional failure (PJF) is a significant mechanical complication following corrective surgery for adult spinal deformity (ASD), often resulting in structural failure at the uppermost instrumented vertebra and necessitating revision surgery. Early identification of patients at high risk for PJF remains clinically important but challenging due to the multifactorial and nonlinear nature of risk factors. This study aimed to evaluate the predictive performance of five machine learning (ML) models-Random Forest, Logistic Regression, Support Vector Machine (SVM), Decision Tree, and Naive Bayes-in identifying patients at risk for PJF using both preoperative and postoperative spinal alignment parameters. A retrospective cohort of 92 ASD patients who underwent two-stage corrective surgery, including lateral lumbar interbody fusion (LLIF), was analyzed. Radiographic parameters were measured preoperatively and approximately six weeks postoperatively. Six alignment-related features were selected through a combination of univariate statistical testing and Random Forest-based feature importance ranking: preoperative PI-LL, preoperative PT, postoperative PI-LL, postoperative and preoperative TK, and change in lumbar lordosis (ΔLL). Each ML model was trained and tested using five independent stratified 80:20 train-test splits. Among the models, Random Forest achieved the highest mean accuracy (78.4%) and area under the curve (AUC = 0.704). The predicted probabilities for PJF were significantly higher in the PJF group compared to non-PJF cases (0.306 ± 0.181 vs. 0.186 ± 0.164, p = 0.0057). Cross-validation confirmed model robustness (fivefold: 79.4%, tenfold: 77.3%). These findings suggest that Random Forest can serve as a reliable tool for preoperative and early postoperative PJF risk stratification based on alignment correction. Future work should incorporate bone mineral density, comorbidities, and multicenter validation to enhance clinical applicability.
Indexed as
Identifiers
What 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.