OC11.04: Fetal surveillance of anti‐Ro/anti‐La affected pregnancies: is there a consensus? Results of an international survey
Bibliographic record
Abstract
Surveillance of pregnancies affected by maternal anti-Ro/ anti-La auto-antibodies (AB) with regard fetal cardiac monitoring is controversial. The aim of this study was to obtain an overview of current practice worldwide. A web-based survey was developed by members of ISUOG Fetal Heart Interest Group. Link to the survey was sent via Newsletter to all ISUOG and Fetal Heart Society members and by direct email to UK fetal cardiologists and members of AEPC fetal working group. Additional emails were sent directly to other worldwide professionals known to manage such pregnancies. There were 92 respondents. About ½ were cardiologists and ½ obstetricians or fetal medicine specialists. Nearly 40% manage <5 cases/year and only ∼10% see >20cases/year. For about half of the respondents, ‘positive’ anti-Ro/La is the only information known in > 90% of cases seen. About one quarter know AB titers and just over 10% are aware of anti-Ro subtypes for > 90% of cases. With no previously affected child, about half use echo and fetal heart rate monitoring and about one quarter use echo alone. Most respondents (∼ 2/3) would start monitoring at 16-20weeks. Frequency of monitoring varied, being every 2 weeks in ∼40%, and weekly in about one quarter of responses. From replies, there was no consensus on how long to monitor the pregnancy for. If 1 degree atrioventricular (AV) block or myocardial abnormalities were found, most (50-60%) would increase frequency of scans and ∼40% would start steroids. Most use left ventricular inflow-outflow Doppler to measure the AV interval, but there is no consensus on how to define 1 degree AV block. With a previously affected child, most would monitor the pregnancy differently but ∼20% would not. Although there were some trends, there was no clear consensus on how to monitor these pregnancies. Evidence-based guidelines are likely to optimise fetal surveillance.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".