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Record W2476997505 · doi:10.12968/bjom.2016.24.8.556

Non-invasive prenatal testing for Down syndrome in general maternity services

2016· article· en· W2476997505 on OpenAlexaff
Victoria Bills, Jenny Ford, Anne M Duffner, Peter Soothill

Bibliographic record

VenueBritish Journal of Midwifery · 2016
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsTrisomyDown syndromeMedicineObstetricsCell-free fetal DNAPrenatal diagnosisPrenatal screeningDna testingFetusAneuploidyPregnancyIntensive care medicinePediatricsBiologyGeneticsPsychiatryChromosome

Abstract

fetched live from OpenAlex

Since its discovery in 1997, the presence of cell-free fetal DNA in the maternal bloodstream has been put to clinical use to detect variety of fetal conditions, in the antenatal period. The use of fetal DNA can offer a highly accurate screen for the presence of Down syndrome (trisomy 21). This has numerous advantages over standard first trimester combined screening for Down syndrome; for example, a reduction in miscarriages due to its non-invasive nature. This article considers a number of issues that need to be resolved before widespread implication of this type of screening into standard NHS practice.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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