Epidemiological Characteristics of Traumatic Spinal Cord Injury in Saudi Arabia: A Systematic Review
Bibliographic record
Abstract
Traumatic spinal cord injury (TSCI) is a severe condition with high mortality and disability rates. Understanding the regional TSCI epidemiology may facilitate the development of targeted preventive initiatives and the optimization of resource allocation. The primary goal of this systematic review was to gather and analyze the existing literature on the frequency and characteristics of TSCI in Saudi Arabia. A literature search of PubMed, Web of Science, and Google Scholar was conducted in January 2024 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Observational studies reporting TSCI epidemiology in Saudi Arabia between 2010 and 2022 were included. Data on demographics, mechanisms, levels/severity, and outcomes were extracted. Methodological quality was assessed using the Newcastle-Ottawa Scale. Nine studies involving 2,356 TSCI cases were analyzed. Most patients were young males. Road traffic accidents were shown to be the predominant cause, accounting for 56.5-90.8% of cases. Thoracic (28.7-48.3%) and cervical (26.6-39%) levels were the most common. The extent of neurological deficits showed significant variation throughout the studies. This review provides a baseline understanding of TSCI epidemiology in Saudi Arabia but highlights critical gaps that future research should address. The review emphasizes the need for evidence-based interventions targeting road safety and falls, standardized cervical spine evaluation and management, and the use of validated metrics to optimize patient outcomes. Large-scale population-based studies with standardized methodologies are necessary to fully understand TSCI epidemiology, prognosis, and long-term disability burden in Saudi Arabia, leading to better prevention strategies and improved patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".