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Record W4360992942 · doi:10.1002/jcla.24870

Non‐Invasive prenatal testing with rolling circle amplification: Real‐world clinical experience in a non‐molecular laboratory

2023· article· en· W4360992942 on OpenAlexaff
Matthew L. Saidel, Uma Ananth, Donna Rose, Cara Farrell

Bibliographic record

VenueJournal of Clinical Laboratory Analysis · 2023
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsPrenatal diagnosisPolymerase chain reactionWorkflowCell-free fetal DNAComputational biologyObstetricsMedicineReal-time polymerase chain reactionComputer scienceBiologyBioinformaticsPregnancyFetusGeneticsGene

Abstract

fetched live from OpenAlex

BACKGROUND: Non-invasive prenatal testing (NIPT) using cell-free DNA (cfDNA) circulating in maternal blood provides a sensitive and specific screening technique for common fetal aneuploidies, but the high cost and workflow complexity of conventional methodologies limit its widespread implementation. A unique rolling circle amplification methodology reduces cost and complexity, providing a promising alternative for increased global accessibility as a first-tier test. METHODS: In this clinical study, 8160 pregnant women were screened on the Vanadis system for trisomies 13, 18, and 21, and positive results were compared to clinical outcomes where available. RESULTS: The Vanadis system yielded a 0.07% no-call rate, a 98% overall sensitivity, and a specificity of over 99% based on available outcomes. CONCLUSION: The Vanadis system provided a sensitive, specific, and cost-effective cfDNA assay for trisomies 13, 18, and 21, with good performance characteristics and low no-call rate, and it eliminated the need for either next-generation sequencing or polymerase chain reaction amplification.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.072
GPT teacher head0.410
Teacher spread0.338 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2023
Admission routes1
Has abstractyes

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