O22. THE RELATIONSHIP BETWEEN PSYCHOLOGICAL DISTRESS AND RHEUMATOLOGICAL CONDITIONS: IS IT A DISEASE-SPECIFIC DISTRESS?
Bibliographic record
Abstract
Background: Long-term autoimmune inflammatory musculoskeletal conditions have psychological, physical and socio-economic implications. Patients are more likely to experience psychological distress (PD), including depression and anxiety. Early studies in type 2 Diabetes and inflammatory bowel diseases have observed disease specific distress (DSD), which is distinct from anxiety and depression. It is direct distress related to the impact and burden of living with a chronic disease and hence DSD can be conceptually and empirically differentiated from symptoms of PD. Although this concept is novel in RA and related conditions, the aim of this study was to explore if there is evidence of illness specific distress in chronic rheumatological conditions. Methods: A thematic secondary qualitative data analysis was undertaken retrospectively from 79 audio-recorded 1:1 interview transcripts from five existing data sets. These included patients with a diagnosis of Rheumatoid arthritis (RA), Myositis and Antiphospholipid syndrome (APS). Each transcript was uploaded onto a qualitative computer software programme. Codes were generated that comprised of mood, fatigue, pain, disability and social impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".