Dual trajectories of fatigue and disease activity in an inception cohort of adults with systemic lupus erythematosus over 10 years
Bibliographic record
Abstract
ObjectivesFatigue is one of the most common symptoms reported in patients living with SLE. We aim to: 1) determine if different trajectories of fatigue associate with specific latent classes of disease activity and 2) define the patient characteristics and associated factors in different latent classes.MethodsData from an inception cohort of adult patients from the Toronto Lupus Clinic from 1997-2018 were analyzed. Fatigue levels were measured using Fatigue Severity Scale (FSS) and disease activity by the Adjusted Mean Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) (AMS). Dual latent class trajectory analysis, for fatigue and AMS, was performed. Univariable and multivariable logistic regression analyses assessed the association of baseline variables with class membership.ResultsAmong 280 patients, 4 dual classes (C) of fatigue and disease activity were identified: C1- lowest disease activity and second highest fatigue trajectory (27%); C2- second highest disease activity and highest fatigue trajectory (30%); C3-moderate disease activity and lowest fatigue trajectory (33%); and C4- highest disease activity and moderate fatigue trajectory (10%).Conclusion4 distinct latent classes of dual fatigue and disease activity trajectories were identified. Fatigue and disease activity follow distinct trajectories and disease activity alone cannot fully explain fatigue trajectories. Trajectories with higher FSS scores were associated with more fibromyalgia and trajectories with higher disease activity were associated with higher cumulative glucocorticoid use. Higher baseline glucocorticoid use was more likely associated with more fatigue while older age at SLE diagnosis was associated with less fatigue.
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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 | 0.001 |
| 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".