MétaCan
Menu
Back to cohort
Record W4289044851

Noninvasive Prenatal Testing for Trisomies 21, 18, and 13, Sex Chromosome Aneuploidies, and Microdeletions: A Health Technology Assessment.

2019· article· en· W4289044851 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsTrisomyMedicineObstetricsPopulationCell-free fetal DNAPrenatal screeningGynecologyPrenatal diagnosisPregnancyFetusGeneticsBiologyEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Pregnant people have a risk of carrying a fetus affected by a chromosomal anomaly. Prenatal screening is offered to pregnant people to assess their risk. Noninvasive prenatal testing (NIPT) has been introduced clinically, which uses the presence of circulating cell-free fetal DNA in the maternal blood to quantify the risk of a chromosomal anomaly. At the time of writing, NIPT is publicly funded in Ontario for pregnancies at high risk of a chromosomal anomaly. METHODS: We completed a health technology assessment, which included an evaluation of clinical benefits and harms, value for money, budget impact, and patient preferences related to NIPT. We performed a systematic literature search for studies on NIPT for trisomies 21, 18, and 13, sex chromosome aneuploidies, and microdeletions in the average-risk or general population. We evaluated the cost-effectiveness of traditional prenatal screening, NIPT as a second-tier test (performed after traditional prenatal screening), and NIPT as a first-tier test (performed instead of traditional prenatal screening). We also conducted a budget impact analysis to estimate the additional costs of funding first-tier NIPT. We interviewed people who had lived experience with NIPT and people living with the conditions NIPT screens for, or their families. RESULTS: The pooled clinical sensitivity of NIPT in the average-risk or general population was 99.5% (95% confidence interval [CI] 81.8%-99.9%) for trisomy 21, 93.1% (95% CI 75.9%-98.3%) for trisomy 18, and 92.7% (95% CI 81.6%-99.9%) for trisomy 13. The clinical specificity for any trisomy was 99.9% (95% CI 99.8%-99.9%). Compared with traditional prenatal screening, NIPT was more accurate in detecting trisomies 21, 18, and 13, and decreased the need for diagnostic testing. We found limited evidence on NIPT for sex chromosome aneuploidies or microdeletions in the average-risk or general population. Positive NIPT results should be confirmed by diagnostic testing.Compared with traditional prenatal screening, second-tier NIPT detected more affected fetuses, substantially reduced the number of diagnostic tests performed, and slightly reduced the total cost of prenatal screening. Compared with second-tier NIPT, first-tier NIPT detected more affected cases, but also led to more diagnostic tests and additional budget of $35 million per year for average-risk pregnant people in Ontario.People who had undergone NIPT were largely supportive of the test and the benefits of earlier, more accurate results. However, many discussed the need for improved pre- and post-test counselling and raised concerns about the quality of the information they received from health care providers about the conditions NIPT can screen for. CONCLUSIONS: NIPT is an effective and safe prenatal screening method for trisomies 21, 18, and 13 in the average-risk or general population. Compared with traditional prenatal screening, second-tier NIPT improved the overall performance of prenatal screening and slightly decreased costs. Compared with second-tier NIPT, first-tier NIPT detected more chromosomal anomalies, but resulted in a considerable increase in the total budget. Interviewees were generally positive about NIPT, but they raised concerns about the lack of good informed-choice conversations with primary care providers and the quality of the information they received from health care providers about chromosomal anomalies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.283
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicPrenatal Screening and DiagnosticsFrench-language works237,207