DOI: 10.5152/eurjrheumatol.2015.0070 Humor in systemic lupus erythematosus
Bibliographic record
Abstract
Objective: Humor has neurophysiological effects influencing the release of cortisol, which may have a direct impact on the immune system. Laughter is associated with a decreased production of inflammatory cytokines both in the general population and in rheumatoid arthritis (RA). Our objective was to explore the effects of humor on serum cytokines [particularly interleukin-6 (IL-6)] and cortisol levels in systemic lupus erythematosus (SLE), after a standard intervention (120 min of visual comedy). Material and Methods: We enrolled 58 females with SLE from consecutive patients assessed in the Montreal General Hospital lupus clinic. The subjects who consented to participate were randomized in a 1:1 ratio to the intervention (watching 120 min of comedy) or control group (watching a 120 min documentary). Measurements of cytokine and serum cortisol levels as well as 24-h urine cortisol were taken before, during, and after the interventions. We compared serum cytokine levels and serum and 24-h urine cortisol levels in the humor and control groups and performed regression analyses of these outcomes, adjusting for demographics and the current use of prednisone. Results: There were no significant differences between the control and humor groups in demographics or clinical variables. Baseline serum levels of IL-6, IL-10, tumor necrosis factor-alpha, and B-cell activating factor were also similar in both groups. There was no evidence of a humor effect in terms of decreasing cytokine levels, although there was some suggestion of lowered cortisol secretion in the humor group based the 24-h urinary cortisol levels in a subgroup. Conclusion: In contrast to what has been published for RA, we saw no clear effects of humor in altering cytokine levels in SLE, although interesting trends were seen for lower cortisol levels after humor intervention compared with the control group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.061 |
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".