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Influence of Age on the Association Between Various Measures of Obesity and All‐Cause Mortality

2009· article· en· W1582257696 on OpenAlexaff
Jennifer L. Kuk, Chris I. Ardern

Bibliographic record

VenueJournal of the American Geriatrics Society · 2009
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineObesityWaistAbdominal obesityBioelectrical impedance analysisBody mass indexNational Health and Nutrition Examination SurveyProspective cohort studyDemographyCohortCohort studyGerontologyInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine whether the association between various simple measures of obesity and risk for all-cause mortality differs between younger and older men and women. DESIGN: Prospective cohort study with 8.7 +/- 0.2 years of follow-up for mortality linkage. SETTING: Third National Health and Nutrition Examination Survey, 1988 to 1994. PARTICIPANTS: Four thousand, four hundred thirty-seven men and 5,166 women. MEASUREMENTS: Measures of obesity included body mass, waist circumference, waist-to-hip ratio, hip circumference, sum of skinfolds, and bioelectrical impedance. RESULTS: Overall and abdominal obesity are associated with greater mortality risk in younger adults (<65) (P<.05), whereas the associations between obesity and mortality are null or inverse in older adults (>65). In general, the association was stronger with measures of abdominal obesity than with measures of overall obesity or fat-free mass. CONCLUSION: The adverse effects of obesity on mortality risk are apparent only in adults younger than 65. Obesity as characterized using several different measures was not generally associated with greater mortality risk in older adults. Although weight loss is beneficial for reducing morbidity in obese adults of any age, it is unclear whether weight loss is equally beneficial for reducing mortality risk in older adults.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.053
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.273
Teacher spread0.247 · 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 teacher head, 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

Citations120
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Geriatrics SocietySame topicDiabetes, Cardiovascular Risks, and LipoproteinsFrench-language works237,207