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Non-invasive Prenatal Testing and the Unveiling of an Impaired Translation Process

2017· article· en· W2614562219 on OpenAlexaff
Blake Murdoch, Vardit Ravitsky, Ubaka Ogbogu, Sarah E. Ali‐Khan, Gabrielle Bertier, Stanislav Birko, Tania Bubela, Jeremy de Beer, Charles Dupras, Meika Ellis, Palmira Granados Moreno, Yann Joly, Kalina Kamenova, Zubin Master, Alessandro R Marcon, Mike Paulden, François Rousseau, Timothy Caulfield

Bibliographic record

VenueObstetrical & Gynecological Survey · 2017
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsMcGill UniversityUniversité LavalUniversité de MontréalTrent UniversityUniversity of Alberta
Fundersnot available
KeywordsMedicineCommercializationProcess (computing)Genetic testingRisk analysis (engineering)Intensive care medicineInternal medicineMarketing

Abstract

fetched live from OpenAlex

(Abstracted from J Obstet Gynaecol Can 2017;39(1):10–17) Noninvasive prenatal testing (NIPT) using cell-free DNA (cfDNA) is an exciting new technology with many potential clinical benefits. However, the commercialization of these tests and the potential economic benefits to the laboratories that perform the testing have led to concern that the limitations are being downplayed and the benefits overstated.

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)0.001
metaresearch head score (Gemma)0.119
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.119
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.083
GPT teacher head0.326
Teacher spread0.243 · 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.

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

Citations3
Published2017
Admission routes1
Has abstractyes

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