Practical applications of teledentistry during the Covid-19 pandemic in ASEAN member states – a systematic review
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to determine the utilisation and adoption of teledentistry based solutions and technologies during the Covid-19 Pandemic in the Asean region. BACKGROUND: Teledentistry is a branch of telemedicine that has rapidly advanced in the last few years and has the potential to provide solutions to oral health problems of patients and locations that do not have prompt and immediate access to a dentist or dental services. The Covid-19 has increased the adaption of all digital health technologies and teledentistry is no exception. METHODOLOGY: The study utilized online databases such as Pubmed (Medline), Scopus (Embase) and CINAHL for the purpose of document search. Newcastle Ottawa (NOS) scale was used to determine the quality of the studies included in our systematic review. PRISMA guidelines were used as the criteria for reporting items in the systematic review. RESULTS: A total of 1297 documents were found after applying the search criteria and the keywords for the selected study. After applying the Prisma guidelines, removal of duplicates and irrelevant entries, 10 studies that were conducted during the Covid-19 pandemic were selected, fitting the inclusion criteria. All the studies included were evaluated for quality and risk of bias through the Newcastle Ottawa scale. Only high-quality studies were included for the final review. CONCLUSION: Teledentistry is a cost-effective solution to screen, diagnose and treat dental patients from a distance. Teledentistry also has the potential to continue seamless continuation of dental education to dental students, during disruptive and non-disruptive periods. ASEAN countries should fully utilise the potential of teledentistry, however sound and effective legislation would be the key first step to achieving that potential.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".