A longitudinal study of distress symptoms and work impairment in immune-mediated inflammatory diseases
Bibliographic record
Abstract
OBJECTIVE: We investigated the association between distress symptoms (pain, fatigue, depression, anxiety) and work impairment in four patient populations: multiple sclerosis (N = 107), rheumatoid arthritis (N = 40), inflammatory bowel disease (N = 136) and psychiatric disorders (N = 167). METHODS: Four waves of data collection were completed over three years. The relationship between distress symptoms and overall work impairment was evaluated with univariate and multivariable quantile logistic regression at the 25th, 50th and 75th percentiles. Models were fit to participant average scores and change scores on distress symptom measures. Covariates included sociodemographic factors, comorbidity, physical disability and cognitive function. RESULTS: In the primary univariate analyses of overall work impairment at the 50th percentile, greater severity of distress symptoms was associated with greater work impairment: pain (average β = 0.27, p < 0.001; change β = 0.08, p < 0.001), fatigue (average β = 0.21, p < 0.001; change β = 0.09, p < 0.001) depression (average, β = 0.35, p < 0.001; change, β = 0.16, p < 0.001), anxiety (average, β = 0.24, p < 0.001; change, β = 0.08, p < 0 0.01). Findings were similar in multivariable analyses. CONCLUSION: Pain, fatigue, depression, and anxiety symptoms are important determinants of work impairment in persons with immune-mediated diseases and persons with psychiatric disorders. Successful clinical management of these symptoms has potential to improve work-related outcomes across IMIDs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".