Evidence map›Paper›PMID 40978602›Full record

ArticleBioinformation2025

Behavioral outcome prediction among children using machine learning.

Samir P V, Aruna Kumari G, Nandini Biradar, Kodali Srija, Debasmita Das, Sukabhogi Anusha

Abstract read
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Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Samir P VDepartment of Pedodontics & Preventive Dentistry, Kalinga Institute of Dental Sciences, KIIT Deemed to be University, Bhubaneswar-751006, India.
Aruna Kumari GDepartment of Pedodontics & Preventive Dentistry, Malla Reddy Dental College for Women- Mallareddy Viswavidyapeeth, Hyderabad, India.
Nandini BiradarDepartment of Conservative dentistry and Endodontics, Private Practitioner, Biradar Dental care, Bidar, Karnataka, India.
Kodali SrijaDepartment of Pedodontics & Preventive Dentistry, Malla Reddy Dental College for Women- Mallareddy Viswavidyapeeth, Hyderabad, India.
Debasmita DasDepartment of Pedodontics and Preventive Dentistry, Hi-Tech Dental College & Hospital, Bhubaneswar, India.
Sukabhogi AnushaDepartment of Pedodontics and Preventive Dentistry, Meghana Institute of Dental Sciences, Nizamabad- 503001, Telangana, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Behavioural management in paediatric dentistry is essential for treatment success, yet predicting a child's behavior remains a challenge. This study used machine learning models on data from 120 children aged 4-10 years, incorporating clinical and historical variables such as age, dental history and parental anxiety. Among the models tested, Random Forest achieved the highest accuracy (87.5%) in predicting behavior based on the Frankl scale. Key predictors of negative behavior included younger age, high parental anxiety and prior negative dental experiences. These findings highlight the potential of machine learning to support behavior guidance planning and improve clinical outcomes.

Indexed as

behavioral predictiondental anxietyfrankl scalemachine learningPediatric dentistryrandom forest

Identifiers

PMID40978602
PMCPMC12449484

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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.