P01.22: Nuchal translucency screening in the detection of Down syndrome
Bibliographic record
Abstract
The aim of this prospective observational study was to evaluate the performance of nuchal translucency screening (NTS) in the detection of fetal Down syndrome (trisomy 21). The study population consisted of unselected consenting patients (single site) having NTS between 11–13 6/7 weeks gestation who agreed to follow-up. All women received pre-and post-screen counseling, and were provided with results at the same visit. All NT measurements were performed by Fetal Medicine Foundation, UK certified sonographers. A screening result was considered positive for Down syndrome if the NT-adjusted risk was greater than or equal to 1 : 300 (Astraia software). During the study period, 10,563 women underwent NTS and are available for follow up. The mean maternal age was 32.6 years, and 31% of the women were ≥ 35 years of age at delivery. At the 1/300 cut-off, the observed detection rate (DR) for Down syndrome was 75.5% at a false positive rate (FPR) of 9%; the DR was 71% at a fixed FPR of 5%. The DR and FPR of NTS in this program are comparable to published values of other programs. Establishing Downs screening in our center as a one-stop, ultrasound-based program has allowed a seamless transition to the one-stop combined screen (NT, PAPP-A, free b-HCG), with the first OSCAR clinic in Canada being established in March 2006.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".