Identifying Smith–Lemli–Opitz syndrome in conjunction with prenatal screening for Down syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it