High rates of depressive symptoms among patients with systemic sclerosis are not explained by differential reporting of somatic symptoms
Bibliographic record
Abstract
OBJECTIVE: Between 36% and 65% of patients with systemic sclerosis (SSc) report symptoms of depression above cutoff thresholds on self-report questionnaires. The objective of this study was to assess whether these high rates result from differential reporting of somatic symptoms related to the high physical burden of SSc. METHODS: Symptom profiles reported on the Center for Epidemiologic Studies Depression Scale (CES-D) were compared between a multicenter sample of 403 patients with SSc and a sample of respondents to an Internet depression survey, matched on total CES-D score, age, race/ethnicity, and sex. An exact nonparametric generalized Mantel-Haenszel procedure was used to identify differential item functioning between groups. RESULTS: Patients with SSc reported significantly higher frequencies (moderate to large effect size; P < 0.01) on 4 CES-D somatic symptom items: bothered, appetite, effort, and sleep. Internet respondents had higher item scores on 2 items that assessed interpersonal difficulties (unfriendly, large effect size; P < 0.01; disliked, large effect size; P < 0.01) and on 2 items that assessed lack of positive effect (happy, moderate effect size; P = 0.01; enjoy, large effect size; P < 0.01). Adjustment of standard CES-D cutoff criteria for potential bias due to somatic symptom reporting resulted in a reduction of only 3.6% in the number of SSc patients with significant symptoms of depression. CONCLUSION: High rates of depressive symptoms in SSc are not due to bias related to the report of somatic symptoms. The pattern of differential item functioning between the SSc and Internet groups, however, suggests some qualitative differences in depressive symptom presentation.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".