Triploidy identified through second‐trimester serum screening
Bibliographic record
Abstract
OBJECTIVES: To describe the maternal serum marker patterns of triploid pregnancies and estimate the second-trimester prevalence of triploidy. METHODS: Forty-two cases of triploidy were identified in six serum screening programmes, five in the United Kingdom, one in Canada. This study describes the serum marker patterns, serum screening results for Down syndrome, trisomy 18 and open neural tube defects, and maternal age of these triploidy cases. The risk cut-off levels were > or = 1 in 250 for Down syndrome, > or =2.5 MoMs alpha-fetoprotein for open neural tube defects and > or =1:100 for trisomy 18 screening. The estimated second-trimester prevalence of triploidy was based on 22 triploidy cases ascertained in 599 934 pregnancies from three routine screening programmes, which attempted complete ascertainment of aneuploidy cases. RESULTS: The observed second-trimester rate of triploidy was 0.37 per 10 000 fetuses. Two different serum marker patterns were seen in triploid pregnancies, distinguished from each other by typically very high or very low levels of total hCG or free beta-hCG. The median maternal ages were respectively 33 years for triploidy with human chorionic gonadotrophin levels < 1.0 MoM, and 26 years for those with hCG levels > or =1.0 MoM. Fifty-seven percent of the pregnancies with a triploid fetus had a risk estimate > or =1:100 for trisomy 18 alone, 10% had an alpha-fetoprotein > or =2.5 MoM, 5% were screen positive for Down syndrome alone, and 19% had an increased risk or positive results for more than one anomaly. CONCLUSION: The simultaneous use of maternal serum tests designed to screen prenatally for Down syndrome, neural tube defects, and an increased risk of trisomy 18 resulted in a screen-positive result for 90% of pregnancies with triploidy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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".