OP0276-HPR DAY-TO-DAY FLUCTUATIONS OF FATIGUE IN SYSTEMIC SCLEROSIS
Bibliographic record
Abstract
Background Systemic sclerosis (SSc) is a rare auto-immune disease with a huge impact on physical health as well as social well-being, with fatigue being the major problem experienced by patients with respect to their well-being [1]. While fatigue is being reported to be fluctuating and unpredictable, the dynamic nature of fatigue is not well understood [1, 2]. Objectives To examine the within-person fluctuations and clinically meaningful changes in fatigue, as well as the within-person association of fatigue and time-varying determinants in SSc. Methods We performed a daily-diary study in adult patients with a clinical diagnosis of SSc. Patients with pulmonary hypertension or severe pulmonary function disturbances (i.e. vital capacity and diffusing capacity for carbon monoxide < 50%) were excluded. During 14 days patients completed daily assessments at four fixed time points (i.e. 9 a.m., 1 p.m., 5 p.m., 9 p.m.) of fatigue severity and time-varying determinants (i.e. negative affect, positive affect, pain, quality of sleep and perceived exertion of physical activity. As proxy for clinical meaningful change in fatigue the probability of acute change(PAC) was assessed, i.e. the chance that change in day-to-day fatigue levels exceeded the minimally clinical important difference for fatigue[3]. Using multilevel models the within-person fluctuations in fatigue and its association with time-varying determinants were examined. Based on the extent of clustering, the time-varying determinants were disentangled into their corresponding levels (within persons (within day as well as across days) and between persons) and added to the multilevel model. Models were adjusted for confounding (i.e. BMI, sex, age, and history of covid-infection) where appropriate. Results Fifty-seven patients with SSc, 35% male with mean(SD) age 54.3(14.6) years, participated. The disease duration was mean(SD) 6.9(4.8) years and 29.8 % was diagnosed with diffuse cutaneous SSc. Eighty percent of all observations were completed. During the study period, change in day-to-day level fatigue exceeded the MCID mean(SD) 5.7(1.9) times. The PAC was mean(SD) 0.44(0.14), ranging from 0.08-0.77. For fatigue a between-person variation of 49% and a within-person variation of 51% was observed. With respect to confounders, only BMI was significant in the models for time-varying negative and positive affect. The final models showed significant within-person association with fatigue fluctuations and changes in time-varying determinants within a day, between days and between patients (Table 1). Conclusion This is the first quantitative study showing that fatigue in SSc is characterized by a dynamic course and that approximately half of the day-to-day fluctuations are clinically meaningful, confirming the results of qualitative studies[2]. Moreover, when patients reported more fatigue than usual, they also reported more pain, more negative affect, less positive affect, more perceived exertion of physical activity, and worse quality of sleep than usual. References [1]Basta F et al., Clin Exp Rheumatol. 2018;36 Suppl 113(4):150-60. [2]Nakayama A et al., J Rheumatol. 2016;43(7):1363-75. [3]Khanna D et al., J Rheumatol. 2008;35(12):2339-43. Disclosure of Interests Arthiha Velauthapillai: None declared, Madelon Vonk Speakers bureau: Boehringer Ingelheim, Bristol-Myers Squibb, GSK, Janssen, MSD, Novartis and Roche, Consultant of: Boehringer Ingelheim and Janssen, Grant/research support from: Research grants from Boehringer Ingelheim, Janssen,Ferrer and Galapagos, Cornelia van den Ende: None declared, Johanna E. Vriezekolk Speakers bureau: Eli Lilly, but not pertaining to this study.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".