COVID-19 and Mortality in the Spinal Cord Injury Population: Examining the Impact of Sex, Mental Health, and Injury Etiology
Bibliographic record
Abstract
Background/Objective: The purpose of this study was to investigate the impact of the COVID-19 pandemic on mortality rates in a community-dwelling spinal cord injury (SCI) population in Ontario. Methods: Using health administrative databases, monthly mortality rates were evaluated pre-pandemic, during the pandemic, and post-pandemic from March 2014 to May 2024. Data were stratified by sex, injury etiology, and mental health status. Group differences were evaluated using t-tests. Autoregressive integrated moving average (ARIMA) models evaluated the pandemic’s impact on mortality rates. Results: A significant increase of 21.4% in mortality rates during the pandemic was found for the SCI cohort. With the exception of the traumatic group, all subgroups also experienced a significant increase in mortality rates (males: 13.9%, females: 31.9%, non-traumatic: 32.3%, mental health diagnoses: 19.6%, and mental health diagnoses: 29.4%). During the pandemic, females had a significantly higher mortality rate than males. The non-traumatic group had higher mortality rates than the traumatic group at all time periods. Individuals with mental health diagnoses had higher mortality rates than those without at the pre-pandemic and pandemic periods. Conclusions: The variation in mortality rates across groups highlights inequitable access to medical care in the SCI population, with further research and interventions 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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".