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Record W1546899109 · doi:10.1002/oby.20105

Does obesity associate with mortality among hispanic persons? Results from the national health interview survey

2013· article· en· W1546899109 on OpenAlexaff
Tapan Mehta, Raymond O. McCubrey, Nicholas M. Pajewski, Scott W. Keith, David B. Allison, Carlos J. Crespo, Kevin R. Fontaine

Bibliographic record

VenueObesity · 2013
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsFrontiers Foundation
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineUnderweightOverweightHazard ratioObesityDemographyConfidence intervalBody mass indexNational Health and Nutrition Examination SurveyProportional hazards modelNational Health Interview SurveyGerontologyEnvironmental healthInternal medicinePopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the association between BMI: kg/m(2) and mortality among Hispanic adults. DESIGN AND METHODS: Eight years (1997-2004) of National Health Interview Survey data linked to public-use mortality follow-up data through 2006 were acquired. Using Cox proportional hazards regression, separate models for two attained age strata (18 to <60 years, ≥60 years) adjusting for sex, smoking, and physical activity with over 38,000 analyzable respondents were fit. RESULTS: Among those aged ≥60 years, underweight (BMI ≤ 18.5) associated with elevated mortality (hazard ratio [HR] = 2.19; 95% confidence interval [CI], 1.38-3.46), whereas overweight (BMI of 25 to <30) and obesity grade 1 (BMI of 30 to <35) associated with reduced mortality (HRs = 0.79; 95% CI, 0.65-0.95 and 0.71; 95% CI, 0.56-0.91), respectively. There were no significant associations between BMI and mortality among the 18 to <60 years attained age strata or among never smokers for either age strata. CONCLUSIONS: Overweight and obesity are not obviously associated with elevated mortality among Hispanic 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)0.005
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 score0.268
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.043
GPT teacher head0.275
Teacher spread0.233 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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