Excess Female Siblings and Male Fetal Loss in Families with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (SLE) occurs more frequently among women than men. We aimed to determine whether the male-female ratio in SLE families is different from what would be expected by chance, and whether excess male fetal loss is found. METHODS: All patients with SLE met the revised American College of Rheumatology classification criteria, while unaffected subjects were shown not to satisfy these same criteria. Putative family relationships were confirmed by genetic testing. Pregnancy history was obtained from all subjects, including unrelated control women. Adjusted Wald binomial confidence intervals were calculated for ratio of boys to girls in families and compared to the expected ratio of 1.06. RESULTS: There were 2579 subjects with SLE, with 6056 siblings. Considering all subjects, we found 3201 boys and 5434 girls (ratio 0.59, of 95% CI 0.576-0.602). Considering only the SLE-unaffected siblings, there were 2919 boys and 3137 girls (ratio 0.93, 95% CI 0.92-0.94). In both cases, the ratio of males to females was statistically different from the known birth rate. Among patients with SLE as well as among their sisters and mothers, there was an excess of male fetal loss compared to the controls. CONCLUSION: Siblings of patients with SLE are more likely than expected to be girls. This finding may be in part explained by excess male fetal loss, which is found among patients with SLE and their first-degree relatives.
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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 | 0.004 |
| 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) | 0.005 | 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".