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Record W4246622469 · doi:10.21203/rs.3.rs-97977/v2

Electronic Health Record-Based Genome-Wide Meta-Analysis and Mendelian Randomization Identify Metabolic and Phenotypic Consequences of Non-Alcoholic Fatty Liver Disease

2021· preprint· en· W4246622469 on OpenAlexafffund
Nooshin Ghodsian, Erik Abner, Connor A. Emdin, Émilie Gobeil, Nele Taba, Mary J. Haas, Nicolas Perrot, Hasanga D. Manikpurage, Éloi Gagnon, Jérôme Bourgault, Alexis St-Amand, Christian Couture, Patricia L. Mitchell, Yohan Bossé, Patrick Mathieu, Marie‐Claude Vohl, André Tchernof, Sébastien Thériault, Amit V. Khera, Tõnu Esko, Benoît Arsenault

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCommon FundNational Human Genome Research InstituteNational Institute on Drug AbuseNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteEuropean CommissionEuropean Regional Development FundFonds de Recherche du Québec - SantéEesti TeadusagentuurNational Cancer InstituteNational Institutes of Health
KeywordsMendelian randomizationFatty liverDiseasePhenotypeBiologyMeta-analysisGeneticsMendelian inheritanceComputational biologyGenomeGeneBioinformaticsMedicineGenetic variantsInternal medicineGenotype

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.017
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.056
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.016
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
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 head0.086
GPT teacher head0.400
Teacher spread0.315 · 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.

Study designMeta-analysis
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

Citations0
Published2021
Admission routes2
Has abstractno

Explore more

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