Alexithymia and immunoendocrine parameters in patients affected by systemic lupus erythematosus and rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: Aim of this study was to evaluate the prevalence of alexithymia in patients affected by SLE or RA and to investigate the correlation between alexithymia and immunoendocrine parameters (PRL, hGH, IL-6 and TNF-alfa). METHODS: Twenty-five patients (12 and 13 affected by SLE and RA, respectively) were enrolled into the study. The Toronto Alexithymia Scale-20 (TAS-20) was administered. PRL, hGH, IL-6 and TNF-alfa levels were measured by commercially available ELISA kits. RESULTS: Alexithymia prevalence (TAS-20 > or = 51) was 54% in RA and 42% in SLE patients. hGH serum levels were 3.1+/-4.2 and 1.1+/-0.9 IU/ml in SLE and RA, respectively. PRL concentration was 18.4+/-6.5 ng/ml and 14.2+/-4.0 ng/ml in SLE and RA patients, respectively (p=0.03). In RA group, TNF-alpha was 20+/-36.2 whereas in SLE it was 4.9+/-12.8 pg/ml (p=0.03); IL-6 serum concentrations were 24.4+/-25.1 and 2.9+/-5.4 pg/ml, in RA and SLE respectively (p=0.004). The serum level of hGH showed slight increase in alexithymic group (A) compared to non alexithymic group (NA) in both SLE and RA patients. PRL serum levels in SLE-A patients was 26.7+/-17.3 ng/ml while in SLE-NA patients was 12.4+/-3.3 ng/ml (p=0.04). In RA patients increased values of IL-6 and TNF-alpha were present in the A group compared to NA group (IL-6: 35.3+/-28 pg/mL vs 3.5+/-3.9 pg/mL, p=0.01; TNF-alpha: 34.7+/-39 pg/mL vs 3.1+/-3.4 pg/mL, p=0.01). CONCLUSIONS: In this preliminary results we found an high prevalence of alexithymia and a correlation between immunoendocrine parameters and alexhytimic features in SLE and RA, suggesting that an immunomodulatory pathway could influence this cognitive style in patients with autoimmune disorders. Other studies should contribute to find a common biological pathway linking alexithymia and autoimmunity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".