Previously Identified Common Post-Injury Adverse Events in Traumatic Spinal Cord Injury—Validation of Existing Literature and Relation to Selected Potentially Modifiable Comorbidities: A Prospective Canadian Cohort Study
Bibliographic record
Abstract
Adverse events (AEs) are common during care in patients with traumatic spinal cord injury (tSCI). Increased risk of AEs is linked to patient factors including pre-existing comorbidities. Our aim was to examine the relationships between patient factors and common post-injury AEs, and identify potentially modifiable comorbidities. Adults with tSCI admitted to a Level I acute specialized spine center between 2006 and 2014 who were enrolled in the Rick Hansen SCI Registry (RHSCIR) and had AE data collected using the Spine Adverse Events Severity system were included. Patient demographic, neurological injury, and comorbidities data were obtained from RHSCIR. Potentially modifiable comorbidities were grouped into health-related conditions, substance use/withdrawal, and psychiatric conditions. Negative binomial regression and multiple logistic regression were used to model the impact of patient factors on the number of AEs experienced and the occurrence of the five previously identified common AEs, respectively. Of the 444 patients included in the study, 24.8% reported a health-related condition, 15.3% had a substance use/withdrawal condition, 8% reported having a psychiatric condition; and 79.3% experienced one or more AEs. Older age (p = 0.004) and more severe injuries (p < 0.001) were nonmodifiable independent variables significantly associated with increased AEs. The AEs experienced by patients were urinary tract infections (42.8%), pneumonia (39.2%), neuropathic pain (31.5%), delirium (18.2%), and pressure ulcers (11.0%). Risk of delirium increased in those with substance use/withdrawal; and pneumonia risk increased with psychiatric comorbidities. Opportunity exists to develop clinical algorithms that include these types of risk factors to reduce the incidence and impact of AEs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".