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Record W2014359589 · doi:10.1080/17477160701453441

Effects of physical activity on pediatric reference data for obesity

2007· article· en· W2014359589 on OpenAlexaffabout
Peter T. Katzmarzyk, Stéphane Tremblay, Rebecca Morrison, Mark S. Tremblay

Bibliographic record

VenueInternational Journal of Pediatric Obesity · 2007
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsStatistics CanadaQueen's University
Fundersnot available
KeywordsPercentileMedicineBody mass indexQuartileObesityNational Health and Nutrition Examination SurveyPhysical activityOverweightDemographyAge groupsGerontologyPhysical therapyEnvironmental healthConfidence intervalInternal medicinePopulationStatistics

Abstract

fetched live from OpenAlex

PURPOSE: To examine the influence of physical activity on pediatric obesity reference data for Canada. METHODS: The sample included 3527 boys and 3554 girls, 6 to 18 years of age, from the 2004 Canadian Community Health Survey: Nutrition component. The heights and weights of the participants were directly measured, and the body mass index was calculated (BMI: kg/m(2)). Physical activity levels were reported using an interviewer-administered questionnaire. Participants were divided into low and high physical activity groups, based on age-specific physical activity levels (lower and upper quartiles). BMI percentiles (25th, 50th, 75th, 85th, 95th) were generated by sex using the LMS method, separately by physical activity groups. RESULTS: There were only minor differences in BMI at the 25th and 50th percentiles between physical activity groups in both boys and girls. However, in boys, the low active group had somewhat higher BMI values at the 85th and 95th percentiles than the high active group after the age of 10 years. In girls, the differences in BMI across groups was similar to that of boys at the 95th percentile, but inconsistent at the other percentiles. CONCLUSION: The results suggest that screening for physical activity may be important for the development of national reference data for 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)0.002
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 score0.055
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.343
Teacher spread0.310 · 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

Citations7
Published2007
Admission routes2
Has abstractyes

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