Self-reported Secondary Health Conditions in Relation to Age and Time Living With Spinal Cord Injury: Results From the Second International Spinal Cord Injury Community Survey
Bibliographic record
Abstract
OBJECTIVE: To describe the proportions of self-reported secondary health conditions, assess the overall health burden of these conditions, and examine their associations with age and time living with traumatic or nontraumatic spinal cord injury/disease (SCI/D) across participating countries in the second International Spinal Cord Injury survey. DESIGN: Cross-sectional, multinational, observational cohort study conducted in 2022-2024. SETTING: Community setting with participants from 31 countries representing all 6 World Health Organization regions. PARTICIPANTS: Individuals with traumatic (n=11,882) and nontraumatic (n=3194) SCI/D aged ≥18 years and living in the community. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Proportions of secondary health conditions. A comorbidity index, based on a multicomponent approach-including the number of co-occurring health problems, their severity, and treatment status-was used as a proxy for overall health burden. Linear mixed model was conducted to examine the associations of age and time since injury with the comorbidity index. RESULTS: The most common secondary health problems worldwide were pain (81.5%), feeling depressed (79%), spasticity/muscle spasm (75.5%), and bowel dysfunction (70.5%). Higher comorbidity indices were observed with increasing age and duration of living with injury in individuals with traumatic SCI/D, but not in those with nontraumatic SCI/D. CONCLUSIONS: Both individuals with traumatic and nontraumatic SCI/D worldwide experience high proportions of secondary health conditions. A significant association between overall health burden, increasing age, and time since injury was, however, observed only among those with traumatic injuries. This finding highlights the potential need for tailored interventions that account not only for the type of injury but also for the individual's age and duration of living with SCI/D.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".