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Percentile distributions of waist circumference for 7–19‐year‐old Polish children and adolescents

2009· article· en· W1982891124 on OpenAlexaboutno aff
L. Ostrowska Nawarycz, Alicja Krzyżaniak, Barbara Stawińska-Witoszyńska, Małgorzata Krzywińska-Wiewiórowska, I Szilágyi-Pagowska, Małgorzata Kowalska, Łukasz J. Krzych, Tadeusz Nawarycz

Bibliographic record

VenueObesity Reviews · 2009
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsPercentileWaistCircumferenceDemographyMedicinePediatricsObesityStatisticsMathematicsGeometry

Abstract

fetched live from OpenAlex

The aim of the study was to develop waist circumference (WC) percentiles in Polish children and youth and to compare these with the results obtained in other countries. The study comprised a random group of 5663 Polish children aged 7-18 years. Smoothed WC percentile curves were computed using the LMS method. The curves displaying the values of the 50th (WC(50)) and the 90th (WC(90)) percentile were then compared with the results of similar studies carried out in children from the UK, Spain, Germany, Turkey, Cyprus, Canada and the USA. WC increased with age in both boys and girls and in all observed age periods the boys were seen to dominate. For 18-year-old Polish boys and girls the values of WC(90) were 86.5 and 78.2, respectively, and were lower than the current criteria developed by the International Diabetes Federation. Both WC(50) and WC(90) were higher in Polish boys and girls compared with their counterparts in the UK, Turkey and Canada and significantly lower than in children from the USA, Cyprus and Spain. The percentile curves for Polish children and youth, which were developed here for the first time, are base curves that can be applied in analysing trends as well as making comparisons with results of similar studies performed in other countries.

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)0.000
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 score0.023
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.296
Teacher spread0.274 · 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

Citations43
Published2009
Admission routes1
Has abstractyes

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