Chronic widespread musculoskeletal pain with or without fibromyalgia: psychological distress in a representative community adult sample.
Bibliographic record
Abstract
OBJECTIVE: To estimate the severity of depression, anxiety, and other symptoms of psychological distress in a representative general population sample of fibromyalgia (FM) cases (FC) compared to pain controls (PC), and to identify strong correlates of depression and anxiety. METHODS: We compared the severity of depression, anxiety, and other symptoms of psychological distress between 2 representative community samples: (1) 74 confirmed FC, and (2) 48 adults with chronic widespread pain (PC) who did not meet the 1990 ACR criteria for FM. Psychological distress was measured using the Centre for Epidemiological Studies Depression (CES-D) Scale, the State-Trait Anxiety Inventory (STAI), and other measures of psychological distress from the literature. Using cutoff scores for CES-D and trait anxiety, we compared demographic and clinical characteristics in those above and below each cutoff score. Simple linear regression was performed to identify factors strongly and independently correlated with depression and trait anxiety. RESULTS: Compared to PC, FC were more symptomatic on virtually all measures of psychological distress. Similarly, individuals who scored above cutoff scores for depression and anxiety had more physical symptoms and had poorer function than those below. Depression and trait anxiety were highly correlated (r = 0.86). In a simple regression model, the best predictors for both depression and trait anxiety were the total number of symptoms and a physical disability score. CONCLUSION: Depression and anxiety are common and frequently severe even among community cases of FM.
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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 | 0.002 |
| 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".