Hyperglycosylated‐hCG (h‐hCG) and Down syndrome screening in the first and second trimesters of pregnancy
Bibliographic record
Abstract
OBJECTIVE: To validate Down syndrome screening protocols that include hyperglycosylated-hCG (h-hCG) measurements. METHODS: Measuring h-hCG in 21 641 fresh first- and second-trimester maternal serum samples, but not for clinical interpretation. Nuchal translucency (NT) and pregnancy associated plasma protein-A (PAPP-A) measurements were available in the first trimester; alpha-fetoprotein (AFP), unconjugated estriol (uE3), and human chorionic gonadotropin (hCG) measurements in the second trimester. RESULTS: Of the 23 first- and 26 second-trimester Down syndrome pregnancies identified, 52 and 65% of h-hCG measurements were above the 95th centile, respectively. At a 3% false positive rate, maternal age, NT, PAPP-A and h-hCG detected 78% of cases (95% CI, 56-93%). Other combinations were consistent with previous modeling utilizing stored samples. A literature summary indicates h-hCG is as strong a marker as free-beta between 10 and 13 weeks' gestation. CONCLUSIONS: Down syndrome screening performance of h-hCG using fresh samples meets published expectations based on stored samples. h-hCG could replace free beta measurements, at gestational ages as early as 10 weeks.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".