ArticleNeurospine2019
Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions.
Article in Neurospine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Multivariable Prediction Models for Traumatic Spinal Cord Injury: A Systematic Review.Topics in spinal cord injury rehabilitation · 2024Pooled it
- Functional outcome prediction after traumatic cervical spinal cord injury using ensemble machine learning: a three‑center validation study.Acta neurologica Belgica · 2026Article
- Towards an understanding of disturbed sleep phenotypes after traumatic spinal cord injury.Journal of rehabilitation medicine · 2026Observational
- Web-based machine learning application for ambulation prognosis in the rehabilitation phase of spinal cord injury: a retrospective multicenter cohort study.Spinal cord · 2026Article
- Advancing Precision Medicine in Degenerative Cervical Myelopathy.Journal of clinical medicine · 2025Review
- Low Back Pain Among Health Sciences Undergraduates: Results Obtained from a Machine-Learning Analysis.Journal of clinical medicine · 2025Article
- SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.Radiology. Artificial intelligence · 2025Article
- GCN5L: a critical target in energy metabolism pathways.Frontiers in cell and developmental biology · 2025Review
- Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation.Frontiers in rehabilitation sciences · 2025Article
- The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review.Brain & spine · 2025Review
- Prediction of poststroke independent walking using machine learning: a retrospective study.BMC neurology · 2024Article
- Machine-learning-based prediction by stacking ensemble strategy for surgical outcomes in patients with degenerative cervical myelopathy.Journal of orthopaedic surgery and research · 2024Article
- Relationship between spinal alignment and functional disability after thoracolumbar spinal fractures: A systematic review.North American Spine Society journal · 2024Article
- Predictive modeling of ambulatory outcomes after spinal cord injury using machine learning.Spinal cord · 2024Article
- SCIseg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury.medRxiv : the preprint server for health sciences · 2024Article
- Deep Learning-Based Prediction Model for Gait Recovery after a Spinal Cord Injury.Diagnostics (Basel, Switzerland) · 2024Article
- Recent trends in spinal trauma management and research.Journal of clinical orthopaedics and trauma · 2024Article
- Orthopedic Frailty Score and adverse outcomes in patients with surgically managed isolated traumatic spinal injury.Trauma surgery & acute care open · 2024Article
- Emerging Technologies within Spine Surgery.Life (Basel, Switzerland) · 2023Review
- Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy.The spine journal : official journal of the North American Spine Society · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.
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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.