Epidemiology and Impact of Spinal Cord Injury in the Elderly: Results of a Fifteen-Year Population-Based Cohort Study
Bibliographic record
Abstract
Although experience suggests a shift in the epidemiology of spinal cord injury (SCI) toward an older demographic, population studies are lacking. We aimed to evaluate (1) how the epidemiology and age profile of SCI have changed over time, and (2) how increased age impacts health outcomes up to 15 years post-injury. A population-based cohort study was performed in Ontario including adults diagnosed with traumatic SCI between 2002 and 2017. Older and younger SCI cohorts were created based on an age cutoff of 65 years. An older cohort of uninjured persons was matched to the older SCI cohort based on age, gender, and comorbidity status. Changes in crude incidence were reported as average annual percentage change (AAPC). Survival, readmissions, and costs were compared between the older and younger SCI cohorts as well as the between the older SCI and older matched uninjured cohorts. The incidence of SCI increased among females (AAPC 2.2; 95% confidence interval [CI] 0.1, 4.3), driven by a marked rise (4%/year) among elderly females (AAPC 4.3; 95% CI 0.1, 4.3). Although no change in incidence was detected for males, there was a trend toward increased incidence among older males (AAPC 1.2; 95% CI -1.3, 3.8). There were a higher proportion of cervical, incomplete, and fall-related injuries in the older than in younger SCI cohorts. Being over 65 years of age was associated with a sixfold increased risk of death (hazard ratio [HR] 5.75; 95% CI 4.72, 7.00). In comparison with the older uninjured cohort, the older SCI cohort had double the risk of death (HR 2.23; 95% CI 2.00, 2.50). Older persons with SCI had higher odds of readmission and higher costs. The incidence of SCI among the elderly is increasing, particularly among women. Prevention through fall reduction and education to improve outcomes are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".