MétaCan
Menu
Back to cohort

Clear detection of ADIPOQ locus as the major gene for plasma adiponectin: Results of genome-wide association analyses including 4659 European individuals

2009· article· en· W2043512342 on OpenAlexaff
Iris M. Heid, Peter Henneman, Andrew A. Hicks, Stefan Coassin, Thomas W Winkler, Yurii S. Aulchenko, Christian Fuchsberger, Kijoung Song, Marie‐France Hivert, Dawn Waterworth, Nicholas J. Timpson, J. Brent Richards, John R. B. Perry, Toshiko Tanaka, Najaf Amin, Barbara Kollerits, Irene Pichler, Ben A. Oostra, Barbara Thorand, Rune R. Frants, Thomas Illig, Josée Dupuis, Beate Glaser, Tim D. Spector, Jack M. Guralnik, Josephine M. Egan, Jose C. Florez, David M. Evans, Nicole Soranzo, Stefania Bandinelli, Olga D. Carlson, Timothy M. Frayling, Keith Burling, George Davey Smith, Vincent Mooser, Luigi Ferrucci, James B. Meigs, Péter Vollenweider, Ko Willems van Dijk, Peter P. Pramstaller, Florian Kronenberg, Cornelia M. van Duijn

Bibliographic record

VenueAtherosclerosis · 2009
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsAdiponectinGenome-wide association studySingle-nucleotide polymorphismGenetic associationMetabolic syndromeLocus (genetics)BiologyPopulationType 2 diabetesInternal medicineGeneticsEndocrinologyGeneInsulin resistanceDiabetes mellitusMedicineGenotype

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)0.002
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.299
Teacher spread0.252 · 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

Citations159
Published2009
Admission routes1
Has abstractno

Explore more

Same venueAtherosclerosisSame topicAdipokines, Inflammation, and Metabolic DiseasesFrench-language works237,207