Health Conditions: Effect on Function, Health-Related Quality of Life, and Life Satisfaction After Traumatic Spinal Cord Injury. A Prospective Observational Registry Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To analyze relations among injury, demographic, and environmental factors on function, health-related quality of life (HRQoL), and life satisfaction in individuals with traumatic spinal cord injury (SCI). DESIGN: Prospective observational registry cohort study. SETTING: Specialized acute and rehabilitation SCI centers. PARTICIPANTS: Participants (N=340) from the Rick Hansen Spinal Cord Injury Registry (RHSCIR) who were prospectively recruited from 2004 to 2014 were included. The model cohort participants were 79.1% men, with a mean age of 41.6±17.3 years. Of the participants, 34.7% were motor/sensory complete (ASIA Impairment Scale [AIS] grade A). INTERVENTIONS: None. MAIN OUTCOME MEASURES: Path analysis was used to determine relations among SCI severity (AIS grade and anatomic level [cervical/thoracolumbar]), age at injury, education, number of health conditions, functional independence (FIM motor score), HRQoL (Medical Outcomes Study 36-Item Short-Form Health Survey [Version 2] Physical Component Score [PCS] and Mental Component Score [MCS]), and life satisfaction (Life Satisfaction-11 [LiSat-11]). Model fit was assessed using recommended published indices. RESULTS: Goodness of fit of the model was supported by all indices, indicating the model results closely matched the RHSCIR data. Higher age, higher severity injuries, cervical injuries, and more health conditions negatively affected FIM motor score, whereas employment had a positive effect. Higher age, less education, more severe injuries (AIS grades A-C), and more health conditions negatively correlated with PCS (worse physical health). More health conditions were negatively correlated with a lower MCS (worse mental health), however were positively associated with reduced function. Being married and having higher function positively affected Lisat-11, but more health conditions had a negative effect. CONCLUSIONS: Complex interactions and enduring effects of health conditions after SCI have a negative effect on function, HRQoL, and life satisfaction. Modeling relations among these types of concepts will inform clinicians how to positively effect outcomes after SCI (eg, development of screening tools and protocols for managing individuals with traumatic SCI who have multiple health conditions).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".