Traumatic Spinal Cord Injury Mortality, 1981–1998
Bibliographic record
Abstract
BACKGROUND: We aim to assess the long-term trend of and identify risk factors for traumatic spinal cord injury (TSCI) mortality from 1981 through 1998 in the state of South Carolina (SC). METHODS: We analyzed data from the TSCI surveillance system in SC. Poisson regression analyses were used to examine trends in TSCI mortality rates across subpopulations of interest. Multiple logistic regression was used to identify risk factors for TSCI mortality. RESULTS: The rate of TSCI mortality was 27.4 per million population between 1981 and 1998. A significant 3% annual decrease in the TSCI mortality rate was found from 1981 through 1998. Specifically, TSCI mortality rates declined the most per year in motor vehicle crashes, males, and whites. Adjusted for covariates, individuals of older ages, black race, with a cervical TSCI, and with a more severe injury, as defined by both Frankel grade and Abbreviated Injury Scale, were associated with higher odds of in-hospital mortality. Females had lower odds of in-hospital mortality than males. CONCLUSION: Although mortality rate is decreasing, TSCI remains a significant public health problem, with SC having higher rates of TSCI mortality than the United States. The association between gender and in-hospital mortality needs further exploration.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".