Headache, Raynaud’s syndrome and serotonin receptor agonists in systemic lupus erythematosus
Bibliographic record
Abstract
There are potential concerns regarding serotonin receptor agonists in SLE patients with migraine, particularly patients with concomitant Raynaud's syndrome. We estimated the prevalence of lupus-related headache and Raynaud's syndrome in the Montreal General Hospital SLE clinic cohort and evaluated the relationship between these two variables in multivariable logistic regression models, controlling for age, sex, race, SLE duration and the presence of lupus anticoagulant and antibodies to cardiolipin and beta2 glycoprotein I. We also assessed, through chart review in those individuals with both Raynaud's syndrome and migraine, a history of serotonin receptor agonist use, and any associated worsening vasospasm. Based on Systemic Lupus Activity Measure (SLAM) scores, the cumulative incidence of lupus-related headache in our sample (n = 391) was 46.1%; the prevalence of Raynaud's syndrome was 49.4%. The adjusted odds ratio (OR) for lupus-related headache and Raynaud's syndrome was 1.7 (95% CI 1.1, 2.5). In addition, there was a strong independent relationship between headache and anti-beta2 glycoprotein I antibodies (adjusted OR 5.6 [95% CI 1.8, 17.0]). The data from our chart review suggest that careful use of serotonin receptor agonists in patients with both Raynaud's syndrome and migraines may be undertaken, although caution would necessitate that these agents not be used in individuals with very severe Raynaud's (eg, digital ulcerations, and so on).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".