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Record W2137534958 · doi:10.1186/1471-2431-14-159

Creation of a reference dataset of neck sizes in children: standardizing a potential new tool for prediction of obesity-associated diseases?

2014· article· en· W2137534958 on OpenAlexaffabout
Sherri L. Katz, Jean‐Philippe Vaccani, Janine Clarke, Lynda Hoey, Rachel C. Colley, Nicholas Barrowman

Bibliographic record

VenueBMC Pediatrics · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsStatistics CanadaChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineObesityPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Neck circumference (NC), is an emerging marker of obesity and associated disease risk, but is challenging to use as a screening tool in children, as age and sex standardized cutoffs have not been determined. A population-based sample of NC in Canadian children was collected, and age- and sex-specific reference curves for NC were developed. METHODS: NC, waist circumference (WC), weight and height were measured on participants aged 6-17 years in cycle 2 of the Canadian Health Measures Survey. Quantile regression of NC versus age in males and females was used to obtain NC percentiles. Linear regression was used to examine association between NC, body mass index (BMI) and WC. NC was compared in healthy weight (BMI < 85th percentile) and overweight/obese (BMI > 85th percentile) subjects. RESULTS: The sample included 936 females and 977 males. For all age and sex groups, NC was larger in overweight/obese children (p < 0.0001). For each additional unit of BMI, average NC in males was 0.49 cm higher and in females, 0.43 cm higher. For each additional cm of WC, average NC in males was 0.18 cm higher and in females, 0.17 cm higher. CONCLUSION: This study presents the first reference data on Canadian children's NC. The reference curves may have future clinical applicability in identifying children at risk of central obesity-associated conditions and thresholds associated with disease risk.

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)0.000
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 score0.010
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations44
Published2014
Admission routes2
Has abstractyes

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