Mental health symptoms in scleroderma during COVID-19: a Scleroderma Patient-centred Intervention Network (SPIN) cohort longitudinal study
Bibliographic record
Abstract
OBJECTIVES: People with systemic sclerosis (SSc) are vulnerable in COVID-19 and face challenges related to shifting COVID-19 risk and protective restrictions. We evaluated mental health symptom trajectories in people with SSc through March 2022. METHODS: The longitudinal Scleroderma Patient-centred Intervention Network (SPIN) COVID-19 cohort was launched in April 2020 and included participants from the ongoing SPIN Cohort and external enrolees. Analyses included estimated means with 95% CIs for anxiety and depression symptoms pre-COVID-19 for ongoing SPIN Cohort participants and anxiety, depression, loneliness, and fear of COVID-19 for all participants across 28 COVID-19 assessments up to March 2022. We conducted sensitivity analyse including estimating trajectories using only responses from participants who completed >90% of items for ≥21 of 28 possible assessments ("completers") and stratified analyses for all outcomes by sex, age, country, and SSc subtype. RESULTS: Anxiety symptoms increased in early 2020 but returned to pre-COVID-19 levels by mid-2020 and remained stable through March 2022. Depression symptoms did not initially change but were slightly lower by mid-2020 compared to pre-COVID-19 and were stable through March 2022. COVID-19 fear started high and decreased. Loneliness did not change across the pandemic. Results were similar for completers and for all subgroups. CONCLUSIONS: People with SSc continue to face COVID-19 challenges related to ongoing risk, the opening of societies, and removal of protective restrictions. People with SSc, in aggregate, appear to be weathering the pandemic well, but health care providers should be mindful that some individuals may benefit from mental health support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".