Physical and Mental Health–Related Quality of Life Trajectories Among People With Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Most studies of health-related quality of life (HRQoL) in multiple sclerosis (MS) have been cross-sectional. The few longitudinal studies have not accounted for potential heterogeneity in HRQOL trajectories. There may be groups of individuals with common physical or mental HRQoL trajectories over time. Identification of early risk factors for membership in trajectories with poor HRQoL would inform on those at risk. We aimed to identify physical and mental HRQoL trajectories among people with MS and early risk factors for membership in the trajectory groups with the worst HRQoL. METHODS: Between 2004 and 2020, we queried NARCOMS participants regarding HRQoL using the RAND-12, demographics, fatigue, and physical impairments (using the Patient-Determined Disease Steps scale). We included participants who were enrolled in the NARCOMS registry within 3 years of MS diagnosis, lived in the United States, reported physician-confirmed MS, and had ≥3 HRQoL observations. We used group-based trajectory modeling to determine whether there were distinct clusters of individuals who followed similar HRQoL trajectories over time. We evaluated whether baseline participant characteristics associated with the probability of trajectory group membership using a multinomial logit model. RESULTS: We included 4,888 participants who completed 57,564 HRQoL questionnaires between 1 and 27 years after MS diagnosis. Participants had a mean (SD) age of 41.7 (9.5) years at diagnosis, and 3,978 participants (81%) were women. We identified 5 distinct physical HRQoL trajectories and 4 distinct mental HRQoL trajectories. Older age at diagnosis, worse physical impairments, and worse fatigue were associated with an increased odds of being in the group with the worst physical HRQoL when compared with being in the other 4 groups. Income ≤$50,000 and no postsecondary education were associated with an increased odds of membership in the group with the lowest mental HRQoL when compared with that in the other 3 groups. DISCUSSION: We identified groups of people with MS who reported similar physical and mental HRQoL trajectories over time. There are early risk factors for membership in the groups with the worst HRQoL that are easily identifiable by clinicians, providing an opportunity for early interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".