The role of teledentistry in improving oral health outcomes and access to dental care: An umbrella review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".