Alexithymia, Depression, Inflammation, and Pain in Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: We previously reported that depression and inflammation have independent effects on pain severity in patients with rheumatoid arthritis (RA). Alexithymia is a personality trait characterized by deficits in cognitive processing and regulation of emotions. A broad association between alexithymia and various health problems has been suggested, including depression, inflammation, and pain. The objective of this study was to examine the independent influence of alexithymia on pain perception and its relationship to depression and inflammation. METHODS: We evaluated 213 RA outpatients who completed self-administered questionnaires, including the Beck Depression Inventory-II (BDI-II) to measure depression severity, the 20-item Toronto Alexithymia Scale (TAS-20) to measure degree of alexithymia, and a visual analog scale to quantify perceived pain. Serum C-reactive protein (CRP) levels were measured to quantify inflammation severity. RESULTS: An initial significant positive association between the TAS-20 score and pain severity (P = 0.01) lost significance after controlling for BDI-II score and CRP level using regression analysis. An interaction was observed among alexithymia, depression, and inflammation with regard to perceived pain. Among those without alexithymia, pain severity increased linearly with the CRP tertile levels regardless of the presence of depression (P < 0.001 for trend). No linear association between pain severity and CRP level was observed among those with alexithymia. Moreover, depressed patients with alexithymia (BDI-II score ≥14 and TAS-20 score ≥61) reported severe pain even at low CRP levels. CONCLUSION: Alexithymia might have a substantial role in pain perception as well as depression in patients with RA. A biopsychosocial approach is essential to achieve better pain control.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".