Autoimmune Disease Incidence Among Women Prenatally Exposed to Diethylstilbestrol
Bibliographic record
Abstract
OBJECTIVE: Animal studies have suggested that prenatal diethylstilbestrol (DES) exposure may alter immune system development and function including antigen self-recognition. A cohort study was conducted to investigate whether prenatal DES exposure might influence the incidence of at least some specific autoimmune diseases in women. METHODS: A group of women who were and were not prenatally exposed to DES have been followed for more than 25 years for numerous health outcomes including autoimmune disease. To verify diagnoses, medical records or physician abstracts were requested for all women who reported a diagnosis of rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), optic neuritis (ON), and idiopathic thrombocytopenic purpura (ITP). Incidence rates of these autoimmune diseases were compared between women who were and who were not prenatally DES-exposed. RESULTS: Overall, there was no increase in verified autoimmune disease among DES-exposed women relative to those who were not exposed (RR 1.2; 95% CI 0.7, 2.1). There was, however, a positive association between prenatal DES exposure and RA among women younger than 45 years (RR 4.9; 95% CI 1.1, 21.6) and an inverse association among women who were 45 years and older (RR 0.1; 95% CI 0.01, 0.7). CONCLUSION: Overall, these data provide little support for an association between prenatal DES exposure and development of autoimmune disease. The implication that such exposure may be related to RA in an unusual age-related manner is based on small numbers of cases and warrants further study.
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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 | 0.001 |
| 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) | 0.002 | 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".