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Record W4313640464 · doi:10.1111/scd.12819

Dental trauma in children and adolescents with attention‐deficit/hyperactivity disorder: A systematic review and meta‐analysis

2023· review· en· W4313640464 on OpenAlexaboutno aff
Victor Zanetti Drumond, Thaynara Nascimento de Oliveira, José Alcides Almeida de Arruda, Ricardo Alves Mesquita, Lucas Guimarães Abreu

Bibliographic record

VenueSpecial Care in Dentistry · 2023
Typereview
Languageen
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAttention deficit hyperactivity disorderObservational studyPsycINFOMeta-analysisStrengthening the reporting of observational studies in epidemiologyMEDLINEClinical psychologyPsychiatryScopus

Abstract

fetched live from OpenAlex

AIM: Attention-deficit/hyperactivity disorder (ADHD) is a childhood neurodevelopmental disorder primarily characterized by inattention and hyperactivity that affects approximately 7.2% of children and adolescents worldwide. This study aimed to assess whether children and adolescents with ADHD were more likely to have dental trauma when compared to their healthy peers. METHODS: This study was reported following the statements proposed in MOOSE (Meta-analyses Of Observational Studies in Epidemiology). PubMed, Web of Science, Scopus, Embase, APA PsycINFO, LILACS, and grey literature were searched until October 2022. Observational studies with a control group were eligible. The risk of bias was assessed using the Newcastle-Ottawa Scale. The meta-analysis was performed using the R language. GRADE (Grading of Recommendations Assessment, Development and Evaluation) was applied. RESULTS: = 18.6% [0.0%-87.5%]). The risk of bias was high. The strength of the evidence was "very low." CONCLUSION: Children and adolescents with ADHD are more likely to have dental trauma than their non-ADHD peers. However, due to limitations in the design of the included studies, a causal relationship cannot be established.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.419
Teacher spread0.353 · 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.

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

Citations5
Published2023
Admission routes1
Has abstractyes

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