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Neck circumference as a complementary measure to identify excess body weight in children aged 13-24 months

2015· article· en· W2226631608 on OpenAlexfundno aff
Daniela dos Santos, Aila Anne Pinto Farias Contarato, Caroline Kroll, Mayte Bertoli, Sandra Ana Czarnobay, Katherinne Barth Wanis Figueirêdo, Silmara Salete de Barros Silva Mastroeni, Marco Fábio Mastroeni

Bibliographic record

VenueRevista Brasileira de Saúde Materno Infantil · 2015
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsAnthropometryMedicineBody mass indexReceiver operating characteristicCircumferenceBody weightExcess weightTape measureDemographyPediatricsMathematicsInternal medicineOverweight

Abstract

fetched live from OpenAlex

Objectives:to analyze the accuracy of neck circumference (NC) as a measure for assessing excess body weight in children aged 13-24 months of life, according to gender.Methods:this is a cross-sectional study comparing the neck circumferences in relation to body mass index (BMI) and other anthropometric measures. The best cut-off point for identifying excess body weight was determined using the Receiver Operating Characteristics curve (ROC curve), according to gender and age groups 13-15 months, 16-19 months and 20-24 months.Results:NC waspositively correlated (p<0.001) with body weight and BMI in both genders, and length in girls (p<0.001). Positive correlations were found between NC and BMI in the three age groups (13-15, 16-19 and 20-24 months) in both boys and girls. The NC cut-off points for boys were 23.6, 23.9 and 24.0 cm, and 23.4, 23.5 and 23.6 cm for girls, for the 1315, 16-19 and 20-24 age groups respectively.Conclusions:NC can be used to screen for excess body weight in children aged 13-24 months. However, further studies with a larger sample will be required in order to complement these results.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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.

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

Citations11
Published2015
Admission routes1
Has abstractyes

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Same venueRevista Brasileira de Saúde Materno InfantilSame topicObesity, Physical Activity, DietFrench-language works237,207