Teledentistry for improving access to, and quality of oral health care: A protocol for an overview of systematic reviews and meta-analyses
Bibliographic record
Abstract
Abstract Digital technologies are becoming essential to address and optimize the suboptimal performance of healthcare systems. Teledentistry involves the use of information and communication technology to improve access to oral health care and the quality of oral health care delivery. Several systematic reviews (SRs) have been conducted to synthesize evidence on the effectiveness of teledentistry but with conflicting results. The aim of this review is to comprehensively summarize available SRs and provide evidence on the impact of teledentistry on access to oral care, patients’ and oral healthcare providers’ outcomes, quality of oral health care and costs. This protocol has been registered with the International Prospective Register of Systematic Reviews (PROSPERO CRD42022373964). Six electronic databases including MEDLINE (Ovid), Embase ( Embase.com ), CINAHL (EBSCO), Web of Science, Cochrane Library and Epistemonikos will be searched for SRs of quantitative, qualitative, and mixed reviews evaluating teledentistry modalities involving both patients and/or oral health care providers (OHCPs). We will include only studies published in English or French. The primary outcomes will be considered from the patients’ perspective (e.g., access to oral health care, patient-reported outcomes, and experiences). The secondary outcomes will include outcomes from patients and OHCPs (e.g., clinical outcomes, safety, behaviors, and costs). Two independent reviewers will perform data screening, data extraction and will assess the quality of included studies using the AMSTAR 2 and ROBIS tools. Data will be synthesized narratively and presented by tables and graphs. We will report any overlap of primary studies in the SRs. A statement on the strength of evidence for each outcome will be provided if possible. This review will inform decision-makers, patients, OHCPs, and researchers on the potential effectiveness, benefits, and challenges of teledentistry and support them in making recommendations for its use. Results will be disseminated through peer-reviewed publications, presentations at conferences, and on social media.
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 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 | 0.078 | 0.132 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.007 |
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".