Multimorbidity in persons with non-traumatic spinal cord injury and its impact on healthcare utilization and health outcomes
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional survey in Canada. OBJECTIVES: To explore multimorbidity (the coexistence of two/more health conditions) in persons with non-traumatic spinal cord injury (NTSCI) and evaluate its impact on healthcare utilization (HCU) and health outcomes. SETTING: Community-dwelling persons. METHODS: Data from the Spinal Cord Injury Community Survey (SCICS) was used. A multimorbidity index (MMI) consisting of 30 secondary health conditions (SHCs), the 7-item HCU questionnaire, the Short Form-12 (SF-12), Life Satisfaction-11 first question, and single-item Quality of Life (QoL) measure were administered. Additionally, participants were grouped as "felt needed healthcare was received" (Group 1, n = 322) or "felt needed healthcare was not received" (Group 2, n = 89) using the HCU question. Associations among these variables were assessed using multivariable analysis. RESULTS: 408 of 412 (99%) participants with NTSCI reported multimorbidity. Constipation, spasticity, and fatigue were the most prevalent self-reported SHCs. Group 1 had a higher MMI score compared to Group 2 (p < 0.001). A higher MMI score correlated with the feeling of not receiving needed care (OR 1.4, 95% CI 1.08-1.21), lower SF-12 (physical/mental component summary scores), being unsatisfied with life, and lower QoL (all p < 0.001). Additionally, Group 1 had more females (p < 0.001), non-Caucasians (p = 0.034), and lower personal annual income (p = 0.025). CONCLUSIONS: Persons with NTSCI have multimorbidity, and the MMI score was associated with increased HCU and worse health outcomes. This work emphasizes the critical need for improved healthcare and monitoring. Future work determining specific thresholds for the MMI could be helpful for triage screening to identify persons at higher risk of poor outcomes.
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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 | 0.001 | 0.001 |
| 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".