Evidence map›Paper›PMID 26153171›Full record

ArticleGlobal journal of health science2015

Child Weight Growth Chart and Its Associated Factors in Birth Cohort of Maku Using a Growth Curve Model and LMS Method.

Seyyed Mohammad Taghi Ayatollahi, Zahra Sharafi, Elham Haem

Open access · hybridAbstract read
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Article in Global journal of health science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 12 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Seyyed Mohammad Taghi Ayatollahi
Zahra Sharafi. sharafiza@sums.ac.ir.
Elham Haem
Shiraz University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfant growth is defined as a positive change in body size over a period of time, and is a sensitive predictor of social health. The most effective way to determine child growth is by measuring birth weight and constructing a weight growth trajectory. Many studies were conducted on the effects of different factors on birth weight, but investigations of these effects on growth trajectory are really sparse. This study analyzes longitudinal data to determine factors affecting growth trajectory and used LMS chart for comparing children. MATERIALS AND

methodsIn a cohort study, 256 neonates born in 2004 in Maku, Iran, were recruited and were followed until 2009.The weight of the neonates were measured at 12 occasions from birth, until the age of 5 years. A growth curve model was used to determine the affecting factors. The parametric LMS method was used to construct the reference centiles curve of the weight (5th, 50th, 95th percentiles).

findingsThe findings show that while controlling the other factors, birth region, breast feeding duration, mother's education and infants' gender significantly influenced the longitudinal weight rate. However, other variables did not reveal any significant association with growth. The growth charts increased rapidly from birth to 5 years of age for both sexes. It was observed that male children grew faster than females, through the first year of age up to 5 years.

conclusionAlthough every child has a growth potential, this capacity could be influenced by various factors and can be compared with other infants through a growth chart. We used longitudinal data to obtain the risk factor of growth trajectory. LMS method was also used for description of growth. Thereafter, the weight chart of Shiraz, southern Iran's corresponding infants, was compared.

Indexed as

Growth ChartsBirth WeightChild, PreschoolCohort StudiesFemaleHumansInfantInfant, NewbornIranMaleReference Standards

Identifiers

PMID26153171
PMCPMC4803886
OpenAlexW1929303963

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.