ArticleeNeuro2023
Spinal Cord Injury AIS Predictions Using Machine Learning.
Article in eNeuro, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses 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
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning-Based Prognostic Models for Functional Outcomes in Spinal Cord Injury: Systematic Review.JMIR medical informatics · 2026Pooled it
- The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review.Spinal cord · 2026Pooled it
- ShuffleLeNet architecture for spinal cord injury classification and level detection.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Brain Morphology in Subacute Spinal Cord Injury: Insights Into Functional Recovery Across Brain Networks.Brain and behavior · 2026Article
- AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges.Journal of medical Internet research · 2026Article
- Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury.BMC medicine · 2025Article
- Determination of Diagnosis and Prognosis in Spinal Cord Injury Using Machine Learning.Korean journal of neurotrauma · 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
- Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients.Journal of clinical medicine · 2024Article
- Can the Initial Parameters of Functional Scales Predict Recovery in Patients with Complete Spinal Cord Injury? A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2024Article
- Serum glucose/potassium ratio as a clinical risk factor for predicting the severity and prognosis of acute traumatic spinal cord injury.BMC musculoskeletal disorders · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study used machine learning to predict The American Spinal Injury Association Impairment Scale (AIS) scores for newly injured spinal cord injury patients at hospital discharge time from hospital admission data. Additionally, machine learning was used to analyze the best model for feature importance to validate the criticality of the AIS score and highlight relevant demographic details. The data used for training machine learning models was from the National Spinal Cord Injury Statistical Center (NSCISC) database of U.S. spinal cord injury patient details. Eighteen real features were used from 417 provided features, which mapped to 53 machine learning features after processing. Eight models were tuned on the dataset to predict AIS scores, and Shapely analysis was performed to extract the most important of the 53 features. Patients within the NSCISC database who sustained injuries were between 1972 and 2016 after data cleaning (
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.