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Record W2164082822 · doi:10.2337/dc06-0441

Waist Girth Does Not Predict Metabolic Complications in Severely Obese Men

2006· article· en· W2164082822 on OpenAlexafffund
Isabelle Lemieux, Vicky Drapeau, Denis Richard, Jean Bergeron, Picard Marceau, Simon Biron, Pascale Mauriège

Bibliographic record

VenueDiabetes Care · 2006
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsMedicineWaistDiabetes mellitusGirth (graph theory)ObesityInternal medicineCardiologyEndocrinologyCombinatoricsMathematics

Abstract

fetched live from OpenAlex

The epidemic of obesity has received considerable attention because of its increasing prevalence and its deleterious impact on health. In this regard, the metabolic syndrome has been recognized as a prevalent cause of cardiovascular disease, and the National Cholesterol Education Program Adult Treatment Panel III guidelines have proposed clinical tools for the identification of individuals characterized by this syndrome. However, there is considerable metabolic heterogeneity among equally overweight/ obese individuals. While some patients show a relatively “normal” metabolic risk profile despite being obese, others who are moderately overweight can nevertheless be characterized by metabolic complications. Thus, it is not uncommon to find severely obese patients with minimal changes in their metabolic risk profile, suggesting that they may be at lower cardiovascular disease risk than what could be expected from their massive obesity. Therefore, the aim of the present study was to examine the relationships between selected features of the metabolic syndrome and waist circumference as a crude marker of abdominal obesity in moderately and severely obese men.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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

Citations43
Published2006
Admission routes2
Has abstractyes

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