The Impact of Co-Morbidities on Age-Related Differences in Mortality after Acute Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Despite the shift in demographics of spinal cord injury (SCI) due to an aging population, relatively little has been reported regarding the effect of age on outcomes after SCI. This study examines the potential confounding effect of co-morbidities on the age-related differences in the hospital mortality following acute traumatic SCI. All consecutive patients with SCI who were admitted to our spine center from 1996 to 2007 were included. Co-morbidities were classified using the Charlson Co-morbidity Index (CCI), Cumulative Illness Rating Scale, and the number of ICD-9 codes. Major potential confounders included age, gender, co-morbidity, and level and severity of SCI. There were 217 males and 80 females with ages from 15 to 96 years. Most patients had an incomplete cervical SCI following falls or motor vehicle accidents. The mean in-hospital mortality rate was 5.7%. Using univariate analyses, older age, relevant pre-existing medical conditions, and motor complete SCI were major risk factors for in-hospital death after acute SCI. Among the three co-morbidity assessments, the CCI was the most reliable co-morbidity index for prediction of hospital mortality in SCI patients after controlling for age in the Cox proportional hazard modeling. In addition, the CCI appears to be a major confounder, which accounts for the majority of age-related differences in mortality following SCI. Our findings have implications for future clinical trials of therapies for adult patients with acute SCI and for management strategies of elderly individuals with SCI.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".