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Record W1573577620 · doi:10.5539/gjhs.v8n2p56

Growth Velocity of Infants From Birth to 5 Years Born in Maku, Iran

2015· article· en· W1573577620 on OpenAlexvenueno aff
Elham Haem, Zahra Sharafi

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHead circumferenceGrowth velocityPercentileGrowth chartMedicineBirth weightAnthropometryBody heightFetal growthDemographyBody weightPediatricsMathematicsStatisticsFetusPregnancyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Growth velocity standards are essential for proper evaluation of child growth. The goal of this study was to construct weight, height and head circumference growth velocity charts for infants. METHODS: This study includes 256 infants (124 boys and 132 girls) born in Maku, Northwest of Iran, and monitored from birth until they were 5 years. The weights and heights of the subjects were recorded at birth, one, two, four, six months and 1, 1.5, 2, 3, 4 and 5 years of age, while the head circumferences were measured until they were 1.5 years old. In this study, the LMS method using LMS chart maker software, was utilized to obtain growth velocity centiles. RESULTS: Growth velocity charts for weight, height and head circumference (5th, 50th, 95th percentiles) were obtained. The velocity growth charts decreased rapidly from birth to 2 years and then remained relatively constant up to 5 years for both sexes. The growth velocity of boys was higher than girls through the first year of age but became equal at 12 months of age and no significant difference was seen up to 5 years. CONCLUSION: Growth velocity studies are really sparse in Iran. In this study, longitudinal data were used to obtain growth velocity centiles. Furthermore, the weight and height velocities of infants from Shiraz, southern Iran, and U.K were compared.

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)0.003
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 score0.034
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.067
GPT teacher head0.371
Teacher spread0.304 · 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
Published2015
Admission routes1
Has abstractyes

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