Psychological distress over 12 months post-diagnosis in an early inflammatory arthritis cohort
Bibliographic record
Abstract
OBJECTIVES: People with inflammatory arthritis (IA) experience worsened mental wellbeing alongside disease progression. Using the National Early Inflammatory Arthritis Audit (NEIAA), we assessed trends in psychological distress during the 12 months following IA diagnosis, mapping these against clinical outcomes to identify associations. METHODS: This is a prospective study of people recruited to NEIAA receiving an IA diagnosis and completing the baseline patient survey. Patient-reported outcomes (PROs) at baseline, 3 months and 12 months were collected, including psychological distress [assessed using Patient Health Questionnaire Anxiety and Depression Screener (PHQ4ADS)]. Mixed effects linear regression models estimated associations between predictor variables with psychological distress at baseline and over time. RESULTS: Of 6873 eligible patients, 3451 (50.2%) showed psychological distress at baseline. Of those completing follow-ups, 30.0% and 24.1% were distressed at 3 months and 12 months, respectively. Higher psychological distress at diagnosis was more commonly reported by younger, female and non-White patients. Clinical factors, including higher counts of comorbidities, prior depression and higher disease activity at diagnosis were associated with higher distress. Higher distress at baseline was associated with poorer outcomes over time in quality of life, disability, work performance, disease activity, as well as reduced likelihood of achieving good treatment response by EULAR criteria. CONCLUSION: Half of patients with IA show significant mental health comorbidity at presentation, which associated with worse disease outcomes and quality of life. Screening for anxiety and depression should be a universal standard, and access to effective mood therapies alongside arthritis treatments is essential. Strategies should be culturally valid and consider multi-morbidities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".