Trial reportJournal of orthopaedic surgery and research2022
A functional outcome prediction model of acute traumatic spinal cord injury based on extreme gradient boost.
Trial report in Journal of orthopaedic surgery and research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03103516 (Epidural Decompression Surgery Within 24 Hours After Acute Spinal Cord Injury Improves Spinal Nerve Function), which is not on this map. Cited by 7 papers.
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.
Epidural Decompression Surgery Within 24 Hours After Acute Spinal Cord Injury Improves Spinal Nerve Function: a Prospective, Multicenter, Non-randomized, Controlled Trial
Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Brain Morphology in Subacute Spinal Cord Injury: Insights Into Functional Recovery Across Brain Networks.Brain and behavior · 2026Article
- Linking neurological status to functional outcomes in spinal cord injury: a multi-class, task-specific approach.BMC biomedical engineering · 2026Article
- Machine Learning Models for Predicting In-Hospital Mortality and Pressure Ulcer Development following Traumatic Spinal Cord Injury; A Cross-sectional Study.Archives of academic emergency medicine · 2026Review
- Determination of Diagnosis and Prognosis in Spinal Cord Injury Using Machine Learning.Korean journal of neurotrauma · 2025Review
- Research on multi-algorithm and explainable AI techniques for predictive modeling of acute spinal cord injury using multimodal data.Scientific reports · 2025Article
- Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.Veterinary surgery : VS · 2025Article
- Application of a novel nested ensemble algorithm in predicting motor function recovery in patients with traumatic cervical spinal cord injury.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed to construct a nonlinear regression model through Extreme Gradient Boost (XGBoost) to predict functional outcome 1 year after surgical decompression for patients with acute spinal cord injury (SCI) and explored the importance of predictors in predicting the functional outcome.
methodsWe prospectively enrolled 249 patients with acute SCI from 5 primary orthopedic centers from June 1, 2016, to June 1, 2020. We identified a total of 6 predictors with three aspects: (1) clinical characteristics, including age, American Spinal Injury Association (ASIA) Impairment Scale (AIS) at admission, level of injury and baseline ASIA motor score (AMS); (2) MR imaging, mainly including Brain and Spinal Injury Center (BASIC) score; (3) surgical timing, specifically comparing whether surgical decompression was received within 24 h or not. We assessed the SCIM score at 1 year after the operation as the functional outcome index. XGBoost was used to build a nonlinear regression prediction model through the method of boosting integrated learning.
resultsWe successfully constructed a nonlinear regression prediction model through XGBoost and verified the credibility. There is no significant difference between actual SCIM and nonlinear prediction model (t = 0.86, P = 0.394; Mean ± SD: 3.31 ± 2.8). The nonlinear model is superior to the traditional linear model (t = 6.57, P < 0.001). AMS and age played the most important roles in constructing predictive models. There is an obvious correlation between AIS, AMS and BASIC score.
conclusionWe verified the feasibility of using XGBoost to construct a nonlinear regression prediction model for the functional outcome of patients with acute SCI, and proved that the predictive performance of the nonlinear model is better than the traditional linear regression prediction model. Age and baseline AMS play the most important role in predicting the functional outcome. We also found a significant correlation between AIS at admission, baseline AMS and BASIC score.
trial registrationClinicalTrials.gov identifier: NCT03103516.
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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.