Impact of introduction of noninvasive prenatal testing on uptake of genetic testing in fetuses with central nervous system anomalies
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of introduction of noninvasive prenatal testing (NIPT) on the uptake of invasive testing in pregnancies complicated by fetal central nervous system (CNS) anomalies. METHODS: Retrospective review of all singleton pregnancies complicated by fetal CNS anomalies seen at a single tertiary center between 2010 and 2017. Cases who had undergone invasive testing or NIPT prior to the diagnosis of the CNS anomaly were excluded. Cases were segregated according to whether they were seen prior to introduction of NIPT (group A, 2010-2013) or thereafter (group B, 2014-2017). We examined the rate of invasive and noninvasive genetic testing in each group. RESULTS: We retrieved 500 cases: 308 (62%) were isolated CNS anomalies, and 192 (38%) had additional structural anomalies. In the total cohort, 165 women (33%) underwent expectant management with no further prenatal genetic testing, 166 (33%) had invasive testing, 52 (10%) had NIPT, and 117 pregnancies (23%) were terminated without further prenatal investigations. The introduction of NIPT significantly decreased the number of pregnancies having no testing (44% group A vs 22% in group B, p < .0001), particularly in the group presenting with isolated ventriculomegaly, but did not affect the uptake of invasive testing (34% vs 32%, respectively; p = .61). NIPT would have missed 4% of pathogenic copy number variants (CNVs) in the group of cases with isolated brain anomalies and 11% of CNVs in cases with complex anomalies. CONCLUSIONS: Uptake of invasive prenatal testing in fetuses with brain anomalies was not affected by NIPT. However, the incidence of no genetic testing was significantly reduced. NIPT was a suboptimal testing strategy in this population as it missed a significant number of subchromosomal genetic anomalies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".