Benefits and pitfalls of prenatal screening in a twin pregnancy
Bibliographic record
Abstract
Prenatal diagnostic testing for aneuploidy is recommended for gravidas of advanced maternal age (AMA) [1]. Screening tests are available when diagnostic testing is declined. Table 1 compares current screening options; as can be seen, fetal aneuploidy screening for AMA women with multiple gestations has inherent limitations. The following case report illustrates some difficulties of aneuploidy screening for AMA women with multiples. A Caucasian couple was referred for genetic counseling for AMA in a dichorionic twin pregnancy at 13 weeks. Consanguinity was denied between the patient, a 38-year old G4P0030 of English, Welsh, and Estonian ancestry and her husband, a 43-year old of French Canadian and Irish ancestry. An age-related Down syndrome risk (DSR) of 1 in 97 was calculated [2]. First trimester sonographic nuchal translucency (NT) screening produced a DSR of 1 in 179 for Twin A and 1 in 164 for Twin B. Combined first trimester screening (FTS) revised the DSR to 1 in 1071. Both the patient and her husband were identified as cystic fibrosis (CF) carriers for the delta F508 mutation; Tay Sachs screening was declined. Second trimester sonogram detected isolated echogenic bowel in Twin B; risks of CF, trisomy, and infection were discussed. Amniocentesis was declined, and the pregnancy continued uneventfully. Following delivery, both twins were found to be homozygous for the CF mutation. Furthermore, Twin B's karyotype was trisomy 21. It is imperative to recognize that genetic disorders are independent events. Although rare, two separate genetic conditions can occur simultaneously. When discussing genetic testing in a twin pregnancy, the options are not as straightforward as for a singleton pregnancy. These limitations must be communicated as well as the possibility that the various screening tests may present conflicting information, necessitating diagnostic testing [3]. Biochemical serum markers in twin pregnancies may produce confusing results. Abnormal protein levels produced by one fetus may be obscured by those produced by the other fetus(es) [3]. In our case, the reassuring revised FTS arose from apparently normal serum results due to the twin gestation. As FTS becomes more popular, screening in multiples should become more dependable.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".