Best practices: antenatal screening for common genetic conditions other than aneuploidy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Carrier screening aims to identify asymptomatic heterozygotes of heritable disorders and has a vital, ever-changing role in its application to the prenatal detection of disease. An explosion of new technologies for the identification of single-gene disorders challenges our ability to evaluate each individual test prior to its introduction into the private and public sectors. RECENT FINDINGS: The efficacy of carrier screening is dependent on several factors including the validity of the test, the incidence of disease within the community, and as many genetic disorders segregate along ethnic and racial lines, which populations should be offered testing. The difficulties in evaluating prenatal screening programs are highlighted by the recent conflicting recommendations from the American College of Medical Genetics and the American Congress of Obstetrics and Gynecology over several single-gene disorders. In addition, changes in the recommendation for universal versus risk-based screening for ethnically segregated disorders remain controversial. These conflicts have major impacts in the provision of genetic counseling in the prenatal outpatient setting. SUMMARY: We will evaluate current and proposed screening protocols for several single-gene disorders, and comment on universal versus ethnic-based screening. Our objective is to develop guidelines for genetic screening in the antenatal outpatient setting.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".