Pregnancy and reproduction in autoimmune rheumatic diseases
Bibliographic record
Abstract
Despite evidence for the important role of oestrogens in the aetiology and pathophysiology of chronic immune/inflammatory diseases, the previous view of an unequivocal beneficial effect of oestrogens on RA compared with a detrimental effect on SLE has to be reconsidered. Likewise, the long-held belief that RA remits in the majority of pregnant patients has been challenged, and shows that only half of the patients experience significant improvement when objective disease activity measurements are applied. Pregnancies in patients with SLE are mostly successful when well planned and monitored interdisciplinarily, whereas a small proportion of women with APS still have adverse pregnancy outcomes in spite of the standard treatment. New prospective studies indicate better outcomes for pregnancies in women with rare diseases such as SSc and vasculitis. Fertility problems are not uncommon in patients with rheumatic disease and need to be considered in both genders. Necessary therapy, shortly before or during the pregnancy, demands taking into account the health of both mother and fetus. Long-term effects of drugs on offspring exposed in utero or during lactation is a new area under study as well as late effects of maternal rheumatic disease on children.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".