Anxiety and Depression in Pediatric-Onset Traumatic Spinal Cord Injury: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Traumatic spinal cord injury (TSCI) is a debilitating neurological condition with significant long-term consequences on the mental health and well-being of affected individuals. We aimed to investigate anxiety and depression in individuals with pediatric-onset TSCI. METHODS: PubMed, Scopus, and Web of Science databases were searched from inception to December 20th, 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, and studies were included according to the eligibility criteria. RESULTS: A total of 1013 articles were screened, and 18 studies with 4234 individuals were included in the final review. Of these, 1613 individuals (38.1%) had paraplegia, whereas 1658 (39.2%) had tetraplegia. A total of 1831 participants (43.2%) had complete TSCI, whereas 1024 (24.2%) had incomplete TSCI. The most common etiology of TSCI with 1545 people (36.5%) was motor vehicle accidents. The youngest mean age at the time of injury was 5.92 ± 4.92 years, whereas the oldest was 14.6 ± 2.8 years. Patient Health Questionnaire-9 was the most common psychological assessment used in 9 studies (50.0%). Various risk factors, including pain in 4 studies (22.2%), reduced sleep quality, reduced functional independence, illicit drug use, incomplete injury, hospitalization, reduced quality of life, and duration of injury in 2 (11.1%) studies, each, were associated with elevated anxiety and depression. CONCLUSIONS: Different biopsychosocial risk factors contribute to elevated rates of anxiety and depression among individuals with pediatric-onset TSCI. Individuals at risk of developing anxiety and depression should be identified, and targeted support should be provided. Future large-scale studies with long-term follow-up are required to validate and extend these findings.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| 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".