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Record W1980448245 · doi:10.1007/s12160-010-9209-1

Television Viewing Time and Risk of Chronic Kidney Disease in Adults: The AusDiab Study

2010· article· en· W1980448245 on OpenAlexaff
Brigid M. Lynch, S. L. White, Neville Owen, Geneviève N. Healy, Steven J. Chadban, Robert C. Atkins, David W. Dunstan

Bibliographic record

VenueAnnals of Behavioral Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsAlberta Health Services
FundersNational Health and Medical Research CouncilBaker Heart and Diabetes InstituteNational Heart Foundation of AustraliaAustralian GovernmentQueensland HealthBristol-Myers SquibbU.S. Department of Health and Human Services
KeywordsAlbuminuriaKidney diseaseMedicineDiabetes mellitusRenal functionOdds ratioObesityRisk factorInternal medicineBody mass indexDiseaseCross-sectional studyProspective cohort studyDemographyEndocrinologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Television viewing time independent of physical activity is associated with a number of chronic diseases and related risk factors; however, its relationship with chronic kidney disease is unknown. PURPOSE: The purpose of this study is to examine the cross-sectional and prospective relationships of television viewing time with biomarkers of chronic kidney disease. METHODS: Participants of the Australian Diabetes, Obesity and Lifestyle Study attended the baseline (n = 10,847) and 5-year follow-up (n = 6,293) examination. RESULTS: Television viewing was significantly associated with increased odds of prevalent albuminuria and low estimated glomerular filtration rate. In the gender-stratified analyses this pattern was seen for men, but not for women. In the longitudinal analyses, odds of de novo albuminuria and low estimated glomerular filtration rate were increased only in unadjusted models. CONCLUSIONS: Television viewing time may be directly related to markers of chronic kidney disease and through intertwined associated risk factors such as diabetes, hypertension, and 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 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)0.001
metaresearch head score (Gemma)0.000
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 score0.151
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.065
GPT teacher head0.400
Teacher spread0.335 · 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

Citations37
Published2010
Admission routes1
Has abstractyes

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