Association between antiphospholipid antibodies and recurrent fetal loss in women without autoimmune disease: a metaanalysis.
Bibliographic record
Abstract
OBJECTIVE: To assess the strength of association between recurrent fetal loss (RFL) and presence of antiphospholipid antibodies (aPL) in women without autoimmune disease, and to examine whether magnitude of association varies according to type or titer of antibody and timing of fetal loss. METHODS: We searched Medline and Current Contents for articles published between 1975 and 2003 with terms denoting early (less than 13 weeks) and late (less than 24 weeks) RFL associated with various aPL. Published case-control, cohort, and cross-sectional studies rated moderate or strong were included in our metaanalysis. Pooled odds ratios with 95% CI were generated using the random-effects models with Cochrane Review Manager software. RESULTS: Our analysis included 25 studies. Lupus anticoagulant (LAC) was associated with late RFL (OR 7.79, 95% CI 2.30-26.45); the association of LAC was stronger than that of any other aPL. IgG anticardiolipin antibodies (aCL), when combining all titers, were associated with both early (OR 3.56, 95% CI 1.48-8.59) and late RFL (OR 3.57, 95% CI 2.26-5.65). Restricting analysis to include only women with moderate to high titers increased the strength of association (OR 4.68, 95% CI 2.96-7.40). It was not possible to extract data on isolated low IgG aCL positivity. IgM aCL were associated with late RFL (OR 5.61, 95% CI 1.26-25.03). There was no association found between early RFL and anti-Beta2-glycoprotein I antibodies (OR 2.12, 95% CI 0.69-6.53). CONCLUSION: The magnitude of the association between aPL and RFL varies according to type of aPL. More data on the relationship between recurrent fetal loss and isolated IgM aCL as well as with low titer IgG aCL would be useful. The place of testing for anti-Beta2-glycoprotein I antibodies remains to be determined.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".