Evidence map›Paper›PMID 39502826›Full record

ArticleFrontiers in public health2024

Development of gross motor skills in children under the age of 3 years: a decision tree approach.

Yuxiang Xiong, Xuhuai Hu, Jindan Cao, Li Shang, Yibei Yao, Ben Niu

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yuxiang Xiong *Department of Medical Informatics, School of Public Health, Jilin University, Changchun, China.
Xuhuai Hu *Shenzhen Health Development Research and Data Management Center, Shenzhen, China.
Jindan CaoDepartment of Medical Informatics, School of Public Health, Jilin University, Changchun, China.
Li ShangShenzhen Health Development Research and Data Management Center, Shenzhen, China.
Yibei YaoDepartment of Medical Informatics, School of Public Health, Jilin University, Changchun, China.
Ben NiuDepartment of Software Technology, School of Computer Science and Software Engineering, Shenzhen Institute of Information Technology, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The early years of life are critical for gross motor development (GMD). This study utilized decision tree modeling to examine the influences on gross motor development in children under the age of 3 years and to rank the key factors affecting their development. Methods: Based on randomized stratified sampling, 9,507 children aged 0-3 years in Shenzhen were included in this study. The Ages and Stages Questionnaires (ASQ) were utilized for the assessment of gross motor development. The chi-square test was used to compare groups, and variables were screened using univariable and multivariable regression analyses. Decision tree modeling was employed to rank the importance of statistically significant variables. Results: The research found a prevalence of gross motor developmental delay of 1.41% among the respondents. The accuracy of the decision tree model is 70.96%. The results demonstrated a strong correlation between seven variables affecting the gross motor development of children, which were ranked based on importance: age, whether to provide supplementary food, average time spent interacting with children, family type, feeding method, mode of delivery, and birth order. Conclusion: The risk of gross motor developmental delay increases with age. Furthermore, supplementary food and interacting with other children are critical factors in improving children's GMD delay. It is therefore imperative to enhance the monitoring of children's gross motor skills through regular developmental assessments that detect potential GMD delays. Moreover, family type, feeding method, mode of delivery, and birth order were also predictive factors of GMD delay.

Indexed as

Child DevelopmentDecision TreesMotor SkillsChild, PreschoolChinaDevelopmental DisabilitiesFemaleHumansInfantInfant, NewbornMalePrevalenceSurveys and Questionnairesdecision treefeature importancegross motorstratified random samplingunder the age of 3 years

Identifiers

PMID39502826
PMCPMC11534659

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Registered trials

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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.