Impact of impairment and secondary health conditions on health preference among Canadians with chronic spinal cord injury
Bibliographic record
Abstract
CONTEXT/OBJECTIVES: To describe the relationships between secondary health conditions and health preference in a cohort of adults with chronic spinal cord injury (SCI). STUDY DESIGN: Cross-sectional telephone survey. SETTING: Community. PARTICIPANTS: Community-dwelling adult men and women (N = 357) with chronic traumatic and non-traumatic SCI (C1-L3 AIS A-D) who were at least 1 year post-injury/onset. INTERVENTIONS: Not applicable. OUTCOME MEASURES: Health Utilities Index-Mark III (HUI-Mark III) and SCI Secondary Conditions Scale-Modified (SCS-M). RESULTS: SCS-M responses for different secondary health conditions were used to create "low impact = absent/mild" and "high impact = moderate/significant" secondary health condition groups. Analysis of covariance was used to examine differences in HUI-Mark III scores for different secondary health conditions while controlling for impairment. The mean HUI-Mark III was 0.24 (0.27, range, -0.28 to 1.00). HUI-Mark III scores were lower (P < 0.001) in high impact groups for spasms, bladder and bowel dysfunction, urinary tract infections, autonomic dysreflexia, circulatory problems, respiratory problems, chronic pain, joint pain, psychological distress, and depression compared with the low impact groups. As well, HUI-Mark III scores were lower (P < 0.05) in high impact groups for pressure sores, unintentional injuries, contractures, heterotopic bone ossification, sexual dysfunction, postural hypotension, cardiac problems, and neurological deterioration than low-impact groups. CONCLUSION: High-impact secondary health conditions are negatively associated with health preference in persons with SCI. Although further work is required, the HUI-Mark III data may be a useful tool for calculating quality-adjusted life years, and advocating for additional resources where secondary health conditions have substantial adverse impact on health.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".