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Record W2989324182 · doi:10.1183/13993003.00514-2019

Can circular RNAs be used as prenatal biomarkers for congenital diaphragmatic hernia?

2019· letter· en· W2989324182 on OpenAlexafffund
Richard Wagner, Aruni Jha, Lojine Ayoub, Shana Kahnamoui, Daywin Patel, Thomas H. Mahood, Andrew J. Halayko, Martin Lacher, Christopher D. Pascoe, Richard Keijzer

Bibliographic record

VenueEuropean Respiratory Journal · 2019
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersInstitute of Human Development, Child and Youth HealthManitoba Lung AssociationCanadian Institutes of Health Research
KeywordsCongenital diaphragmatic herniaDiaphragm (acoustics)Prenatal diagnosisDiaphragmatic herniaMedicineDiaphragmatic breathingLungHerniaBioinformaticsPathologySurgeryInternal medicineFetusPregnancyBiologyGenetics

Abstract

fetched live from OpenAlex

Circular RNAs are dysregulated in lungs of congenital diaphragmatic hernia patients, a malformation of the lung and diaphragm. These results suggest that they can serve as prenatal biomarkers to improve prognostication and diagnostic accuracy.http://bit.ly/2Cz7Bzm

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)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0130.009
Insufficient payload (model declined to judge)0.0040.006

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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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