Maternal Anti‐Ro Antibody Titers Obtained With Commercially Available Immunoassays Are Strongly Associated With Immune‐Mediated Fetal Heart Disease
Bibliographic record
Abstract
OBJECTIVE: Anti-Ro antibody-positive mothers are frequently referred for serial echocardiography due to the fetal risk of developing heart block and endocardial fibroelastosis. Little is known why only some and not all offspring develop these cardiac manifestations of neonatal lupus (CNL). This prospective study examined associations between anti-Ro antibody titers and fetal CNL. METHODS: Antibody-positive mothers referred since 2018 for fetal echocardiography at risk of CNL (group 1; n = 240) or with CNL (group 2; n = 18) were included. Maternal antibody titers were measured with a chemiluminescent immunoassay (CIA). Additional testing on diluted serum samples was used to quantify anti-Ro 60 antibody titers above the analytical measuring range (AMR) of the standard CIA (≥1,375 chemiluminescent units [CU]). RESULTS: Among 27 total mothers with a fetal diagnosis of CNL, all displayed anti-Ro 60 antibody titers that exceeded the AMR of the CIA at least 10-fold. Of 122 mothers in group 1 who underwent additional anti-Ro 60 antibody testing, event rates of CNL (n = 9) were 0% (0 of 45) among mothers with anti-Ro 60 antibody titers from 1,375-10,000 CU, 5% (3 of 56) among mothers with titers from 10,000-50,000 CU, but 29% (6 of 21) among mothers with titers >50,000 CU (odds ratio 13.1, P = 0.0008). Of mothers in group 2 with a primary diagnosis of CNL, 0% (0 of 18 mothers) had anti-Ro 60 antibody titers <10,000 CU, 44% (8 of 18 mothers) had titers from 10,000-50,000 CU, and 56% (10 of 18 mothers) had titers >50,000 CU. CONCLUSION: CNL is associated with substantially higher anti-Ro antibody titers than are obtained using a standard CIA. Enhancing the assay measuring range allows an improved specificity of identifying pregnancies at risk of CNL.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".