Self-reported flaring varies during the menstrual cycle in systemic lupus erythematosus compared with rheumatoid arthritis and fibromyalgia
Bibliographic record
Abstract
OBJECTIVE: We studied self-reported flares before menses in SLE, RA and FM, and determined whether there were differences. METHODS: Part 1: women blinded to study hypothesis having menses with SLE and RA completed a 100-day diary logging their pain, fatigue and disease activity on a 100-mm visual analogue scale (VAS) and menses. Part 2: SLE, RA and FM patients were mailed a questionnaire about menstrual cycle and disease changes. RESULTS: Part 1: 28 patients with SLE and 21 with RA were included; 84% of SLE and 71% of RA patients had regular menses. Patients with SLE had higher pain, fatigue and disease activity during menses than in the hormonal surge phase. Patients with RA had increased pain, fatigue and disease activity during decreasing progesterone. Part 2: 498 patients were surveyed, of whom 56% responded (81 SLE, 136 RA and 61 FM). Those taking the oral contraceptive pill (OCP) ever since diagnosis were 52% with SLE, 41% with RA and 33% with FM (P = 0.1). Those who flared before menses when not on OCP were 36% with SLE, 28% with RA and 54% with FM (P = 0.08). In SLE patients, the mean VAS scores were worse during menses with average scores of 21.0 for pain, 26.7 for fatigue and 18.2 for disease activity vs 16.0 (P = 0.04), 18.6 (P = 0.004) and 11.4 (P = 0.01) during the surge. In RA, the decreasing progesterone phase was different from the increasing oestrogen phase for pain (P = 0.06). CONCLUSION: There could have been recall bias and participants may have confused pre-menstrual syndrome with flares. However, there seem to be menstrual cycle flares in SLE, RA and FM.
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 | 0.001 | 0.006 |
| 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 | 0.000 | 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".