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Record W4401591453 · doi:10.1111/joor.13836

The role of teledentistry in improving oral health outcomes and access to dental care: An umbrella review

2024· review· en· W4401591453 on OpenAlexaff
Diana Al‐Buhaisi, Sanaz Karami, Noha Gomaa

Bibliographic record

VenueJournal of Oral Rehabilitation · 2024
Typereview
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsChildren’s Health Research InstituteLawson Health Research InstituteWestern University
Fundersnot available
KeywordsMedicineMEDLINECritical appraisalSystematic reviewScopusOral healthHealth careFamily medicineNursingAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: This umbrella review aims to synthesise and summarise the role of teledentistry in improving oral health outcomes and access to dental care. METHODS: We searched the databases PubMed (Medline), Scopus, CINHAL, OVID, ScienceDirect, JSTOR, JBI Database of Systematic Reviews & Implementation Reports and the Cochrane Database of Systematic Reviews through March 2024. Systematic reviews and meta-analyses on teledentistry were eligible for inclusion. No publication time or language restrictions were applied. Our search retrieved 24 studies for which we conducted quality assessments using the Joanna Biggs Institute (JBI) for Systematic Reviews and Research Synthesis Critical Appraisal Tool. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Studies addressed clinical oral health outcomes, health-related quality of life and patient experience, access to dental care and cost-effectiveness of teledentistry compared to conventional, face-to-face dental consultations. We found that there was consensus that teledentistry enhanced oral health through the early detection of oral lesions and increased access to dental care in remote areas and was time- and cost-saving. CONCLUSION: Teledentistry can improve oral health outcomes and access to dental care. Future research on its impact on oral health equity is warranted.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.502
Teacher spread0.426 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations27
Published2024
Admission routes1
Has abstractyes

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