Identifying Smith–Lemli–Opitz syndrome in conjunction with prenatal screening for Down syndrome
Bibliographic record
Abstract
BACKGROUND: Smith-Lemli-Opitz syndrome (SLOS) is a rare hereditary disorder of cholesterol metabolism. We examine the feasibility of identifying SLOS as a part of a routine prenatal screening and evaluate diagnostic testing in maternal urine (or serum), in addition to amniotic fluid. METHODS: Our SLOS risk algorithm utilized three Down syndrome screening markers (estimated 62% detection rate; 0.3% screen-positive rate). Fifteen North American prenatal screening programs implemented this algorithm. RESULTS: SLOS risk was assigned to 1 079 301 pregnancies; 3083 were screen-positive (0.29%). Explanations were found for 1174, including 914 existing fetal deaths. Among the remaining pregnancies, 739 were screen-positive only for SLOS; 1170 were also screen-positive for other fetal disorders. Five of six SLOS pregnancies (83%) were screen-positive. All six had sonographic findings, were biochemically confirmed, and were terminated. Maternal urine steroid measurements were confirmatory in four cases tested. Second-trimester prevalence among Caucasians was 1 in 101 000 (1 in 130 000 overall; no cases in other racial groups). Among 739 pregnancies screen-positive only for SLOS, two cases were identified; another 69 had major fetal abnormalities. CONCLUSIONS: Although SLOS occurred less often than previously reported, many other major abnormalities were detected. Implementing the algorithm as an adjunct to Down syndrome screening may be feasible.
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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 | 0.005 |
| 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) | 0.001 | 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".