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Record W2898166546 · doi:10.1111/ipd.12441

Does oral health influence school performance and school attendance? A systematic review and meta‐analysis

2018· review· en· W2898166546 on OpenAlexaboutno aff
Maria Augusta Bessa Rebelo, Janete Maria Rebelo Vieira, Juliana Vianna Pereira, Larissa Neves Quadros, Mário Vianna Vettore

Bibliographic record

VenueInternational Journal of Paediatric Dentistry · 2018
Typereview
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAttendanceMeta-analysisOral healthOdds ratioMEDLINEFamily medicineScopusEpidemiology

Abstract

fetched live from OpenAlex

AIM: To examine the evidence on the influence of oral health status on school performance and school attendance in children and adolescents. DESIGN: A systematic review was performed in accordance with PRISMA included epidemiological studies that assessed concomitantly oral health measures, participants' school performance and/or school attendance. Electronic search was conducted on MEDLINE, SCOPUS, Web of Science, ScienceDirect, and LILACS. Studies published up to May 2018 in any language were eligible. The risk of bias was assessed using the Newcastle-Ottawa Scale. Meta-analysis was used to obtain pooled estimates between oral health measures and school performance and school attendance. RESULTS: Eighteen studies were included. Of them, fifteen studies were used for the meta-analyses. Most studies were assessed as moderate quality. Children with one or more decayed teeth had higher probability of poor school performance (OR = 1.44 95%CI: 1.24-1.64) and poor school attendance (OR = 1.57 95%CI: 1.08-2.05) than caries-free children. Poor parent's perception of child's oral health increased the odds of worse school performance (OR = 1.51 95%CI: 1.10-1.92) and poor school attendance (OR = 1.35 95%CI: 1.14-1.57). CONCLUSIONS: Children and adolescents with dental caries and those reporting worse oral health experience poor school performance and poor school attendance.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.387
Teacher spread0.350 · 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

Citations137
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Paediatric DentistrySame topicDental Health and Care UtilizationFrench-language works237,207