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Record W4410043832 · doi:10.1101/2025.04.29.25325745

Screen Time as a factor for Attention Deficit Hyperactivity Disorder (ADHD) in children: A Systematic Review

2025· review· en· W4410043832 on OpenAlexaff
Um Ul Baneen Zehra, Emaan Tindyala, K Venkatesh, Iram Mansoor, Fateema Tanveer, M. Aslam, Rekhum Sadia

Bibliographic record

VenuemedRxiv · 2025
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsAttention deficit hyperactivity disorderPsychologyAttention deficitFactor (programming language)PsychiatryClinical psychologyDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

Abstract This study examined the relationship between children’s screen time and the potential development of Attention Deficit Hyperactivity Disorder (ADHD). Through analysis of various credible sources over the past decade, including Google Scholar and PubMed Central, a significant correlation between increased screen time and the exacerbation of ADHD symptoms was identified. The research suggested that prolonged screen exposure heightened the risk of developing ADHD and intensified its severity. Additionally, excessive screen usage was found to disrupt sleep patterns and undermine healthy eating habits. This review emphasized the importance of effectively managing ADHD symptoms to potentially mitigate the likelihood of its onset and enhance the overall well-being of children diagnosed with the disorder.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.363
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venuemedRxiv→Same topicAttention Deficit Hyperactivity Disorder→French-language works237,207→