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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 OpenAlex
Richard Wagner, Aruni Jha, Lojine Ayoub, Shana Kahnamoui, Daywin Patel, Thomas H. Mahood, Andrew J. Halayko, Martin Lacher, Christopher D. Pascoe, Richard Keijzer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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

<b>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.</b>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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.023
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