Health Care Utilization in Persons with Traumatic Spinal Cord Injury: The Importance of Multimorbidity and the Impact on Patient Outcomes
Bibliographic record
Abstract
BACKGROUND: Persons with spinal cord injury (SCI) living in the community have high health care utilization (HCU). To date, the interrelationships among multiple secondary health conditions (multimorbidity due to comorbidities and complications) that drive HCU and their impact on patient outcomes are unknown. OBJECTIVE: To determine the association among multimorbidity, HCU, health status, and quality of life. METHODS: Community-dwelling persons with traumatic SCI participated in an online/phone SCI Community Survey. Participants were grouped using the 7-item HCU questionnaire (group 1 did not receive needed care and/or rehospitalized; group 2 received needed care but rehospitalized; group 3 received needed care and not rehospitalized). Personal, injury, and environmental factors; multimorbidity (presence/absence of 30 comorbidities/ complications); health status (Short Form-12); and quality of life measures (Life Satisfaction-11 first question and single-item quality of life measure) were collected. Associations among these variables were assessed using multivariate analysis. RESULTS: The 1,137 survey participants were divided into 3 groups: group 1 (n = 292), group 2 (n = 194), and group 3 (n = 650). Group 1 had the greatest number of secondary health conditions (15.14 ± 3.86) followed by group 2 (13.60 ± 4.00) and group 3 (12.00 ± 4.16) (P < .05). Multimorbidity and HCU were significant risk factors for having a lower SF-12 Mental (P < .001) and Physical Component Score (P < .001). They in turn were associated with participants reporting a lower quality of life (P < .001, for both questions). CONCLUSIONS: Multimorbidity and HCU are interrelated and associated with lower health status, which in turn is associated with lower quality of life. Future work will include the development of a screening tool to identify persons with SCI at risk of inappropriate HCU (eg, rehospitalization, not able to access care), which should lead to better patient outcomes and cost savings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".