Epidemiology of Spinal Cord Injury in British Columbia, Canada: 20 Years of Population-Based Administrative Data
Bibliographic record
Abstract
Traumatic spinal cord injury (TSCI) is a debilitating condition that can have significant effects on physical function and overall quality of life. Mechanisms of injury can vary from major trauma to low-energy falls. There has been a recent increase in the number of elderly patients with TSCI. A retrospective analysis of population-based hospital records linked with health care administrative datasets was conducted to measure age-standardized rates of TSCI over time. The study was conducted to describe the epidemiology and demographic characteristics of patients who experienced TSCI between 2001 and 2021 in the province of British Columbia, Canada. Demographic, clinical characteristics, and rates of TSCI were evaluated over time. Linear regression was used to assess changes over time. The study identified 3622 patients with TSCI. The average age at the time of injury was 51.1 (standard deviation [SD] 21.19) and 75.0% were males. The average annual age-standardized rate in this population was 35.4 per million. The overall rate remained stable throughout the study period. The mean age at injury increased from 41.9 to 57.5 over the study period of 2001–2021 (p < 0.001). The most frequent causes of injury were low-energy falls (49.9%) and motor vehicle injuries (36.6%). The proportion of injuries related to falls increased over the study period (p < 0.001). Motor and sensory complete TSCI were seen in higher rates among younger patients, and cervical spine injuries were most common among all age-groups. The rate of TCSI was consistent during the study period, though the demographic of patients and their injury mechanism changed considerably; elderly low-energy falls were an increasing proportion of cases. Continued vigilance in elderly fall prevention is needed to reduce the incidence of TCSI among the elderly.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".