The Association of Illness-related Uncertainty With Mental Health in Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
Objective Patients with systemic autoimmune rheumatic diseases (SARDs) face illness-related uncertainty, but little is known about the psychological profiles and psychosocial and health needs associated with uncertainty among adults with SARDs. Methods Patients from the Massachusetts General Hospital with antineutrophil cytoplasmic antibody-associated vasculitis (AAV), IgG4-related disease (IgG4-RD), and systemic sclerosis (SSc) completed the Mishel Uncertainty in Illness Scale, 8-item Patient Health Questionnaire depression scale, 7-item General Anxiety Disorder scale, Sickness Impact Profile, and a survey of psychosocial needs. The associations of uncertainty and self-reported needs with depression, anxiety, and sickness impact were assessed. Results One hundred thirty-two patients with AAV (n = 41, 31%), IgG4-RD (n = 61, 46%), or SSc (n = 30, 23%) participated. The mean age was 64 years, 52% were female, and 83% were White. Greater illness-related uncertainty was positively correlated with higher levels of depression (r= 0.43,P< 0.001), anxiety (r= 0.33,P< 0.001), and sickness impact (r= 0.28,P= 0.001). We observed variations in these measures across SARDs, such that uncertainty was more strongly associated with depression and sickness impact in AAV or SSc compared to IgG4-RD. The primary needs that patients endorsed were services for managing physical symptoms (53%), self-care (37%), and emotional concerns (24%), with greater needs strongly associated with greater illness-related uncertainty. Conclusion Among patients with SARDs, illness-related uncertainty is correlated with levels of depression, anxiety, and sickness impact, as well as psychosocial needs. Findings also implicate the need for targeted interventions to address uncertainty and needs among subgroups of patients with different illness profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".