Maternal Serum Screening in Ontario Using the Triple Marker Test
Bibliographic record
Abstract
OBJECTIVES: To summarise the experience and evaluate the performance of the Ontario maternal serum screening (MSS) programme. SETTING: The Ontario MSS programme between October 1993 and September 2000. METHODS: This study used information collected in the Ontario MSS database, which contains data on each screened pregnancy. In the Ontario MSS programme, women are screened between 15 and 20 weeks of gestation. The risk cut-off for Down's syndrome was >or= 1 in 385 at term and women with a serum alpha-fetoprotein >or= 2.2 multiples of the unaffected population median were defined as screen-positive for open neural tube defects. RESULTS: Between 1 October 1993 and 30 September 2000, 428410 women residing in Ontario were screened for open neural tube defects, and 423895 women were screened for Down's syndrome and trisomy 18. Approximately 48% of all pregnant women in the province had MSS. The uptake rate of amniocentesis following a positive Down's syndrome screening was 67%. Of 717 cases of Down's syndrome ascertained in the screened population, 531 were detected by MSS, giving a term detection rate (DR) of 70.6%, with a false-positive rate (FPR) of 7.2%. For neural tube defects, the DR was 72.7%, with a FPR of 2.0%. The screen also detected 50% of cases of trisomy 18 at term, with a FPR of 0.2%. Coincidentally, 113 cases of chromosome aneuploidies other than Down's syndrome and trisomy 18 were detected. DISCUSSION: In the Ontario MSS programme, MSS performed as expected in the detection of Down's syndrome, open neural tube defects and trisomy 18. MSS is an effective and practical method for large-scale second trimester screening for Down's syndrome, open neural tube defects and trisomy 18, and the MSS database is an extremely useful tool in monitoring the performance of this screen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".