Joint trajectories of disease activity, and physical and mental health-related quality of life in an inception lupus cohort
Bibliographic record
Abstract
OBJECTIVES: To examine for latent patterns of SLE disease activity trajectories that associate with specific latent patterns of health-related quality of life (HRQoL; Medical Outcomes Study Short Form-36), and to determine baseline predictors of class membership. METHODS: In this retrospective longitudinal inception cohort of 222 SLE adults over 10 years, trajectories of three outcomes were studied jointly: Short Form-36 physical (PCS) and mental (MCS) component summaries and adjusted mean SLEDAI-2000 (AMS). Group-based joint trajectory modelling was used to model latent classes; univariable and multivariable analyses were used to identify predictors of class membership. RESULTS: Four latent classes were identified: Class 1 (C1) (24%) had moderate AMS, and persistently low PCS and MCS; C2 (26%) had low AMS, moderate PCS and improved then worsened MCS; C3 (38%) had moderate AMS, and persistently high PCS and MCS; and C4 (11%) had high AMS, moderate-low PCS and improving MCS. Baseline older age was associated with lower HRQoL trajectories. Higher AMS trajectories did not associate with a particular pattern of HRQoL trajectory. A higher prevalence of fibromyalgia (44% in C1) was associated with worse HRQoL trajectories. Disease manifestations, organ damage and cumulative glucocorticoid were not differentially distributed across the latent classes. CONCLUSION: High disease activity did not necessarily associate with low HRQoL. More patients with worse HRQoL trajectories had fibromyalgia. Older age at diagnosis increased the probability of belonging to a class with low HRQoL trajectories. The care of SLE patients may be improved through addressing fibromyalgia in addition to disease activity.
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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 | 0.002 |
| 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 | 0.001 |
| 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".