MétaCan
Menu
Back to cohort

Fat chance: genetic syndromes with obesity

2004· review· en· W1490107818 on OpenAlexaff
M‐A Delrue, JL Michaud

Bibliographic record

VenueClinical Genetics · 2004
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsObesityHeritabilityAffect (linguistics)MedicineAdipose tissueBioinformaticsBiologyGeneticsPsychologyEndocrinology

Abstract

fetched live from OpenAlex

Although obesity shows high heritability, we are aware of only a small number of genes that affect adipose mass in humans. Genetic syndromes with obesity represent unique opportunities to gain insight into the control of energy balance. The majority of obesity syndromes can be distinguished by the presence of mental retardation. We performed a systematic search of such syndromes and reviewed the literature with a focus on distinguishing clinical features, the characteristics of their obesity, and the underlying pathogenetic mechanisms. We predict that the study of these conditions will shed light on common forms of obesity.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.140
GPT teacher head0.418
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations52
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueClinical GeneticsSame topicPancreatic function and diabetesFrench-language works237,207