The role of teledentistry in improving access to care for patients with special health-care needs
Bibliographic record
Abstract
Teledentistry has been described as the use of telehealth systems and methodologies in dentistry. The technology for remote telephonic consults in dentistry has existed for nearly as century; however, it was the Internet revolution of the 1990s that saw the first formal investigations to the accuracy and reliability of teledentistry.[1] The COVID-19 pandemic changed many things about the practice of dentistry globally. At the onset of the pandemic, dental regulators around the world permitted the use of teledentistry to screen patients and offer emergency consults. However, it has become apparent that the uses of teledentistry have become more acceptable as the pandemic has progressed. One of the greatest beneficiaries of this acceptance of teledentistry has perhaps been the dental care of individuals with special health-care needs. The provision of dental care to individuals with special health-care needs has fallen on different dental specialties across the globe. Despite the growth of special care dentistry as a recognized specialty across the globe, the number of specialists remains few and far between. The use of teledentistry can help bridge the deficit in several ways. It has been estimated that only about 5%–10% of individuals with special health-care needs require a hospital setting for their oral health care.[2] However, the lack of an effective triage system results in hospital oral health units being overwhelmed with cases, which in turn result increased wait times for the patient. Teledentistry allows for the virtual consultation between both patient and dentist, as well as the transmission of records between general dentist and specialist.[1] This allows for a safe and effective determination of the type of care that can be provided in the dental office by the general dentist and facilitates referral in those cases that need advanced dental care or medical support. The second barrier to providing effective care to patients with special health-care needs is the time it takes dentists to gather relevant medical history and makes an honest assessment of their own comfort with treating the patient. Teledentistry facilitates this first visit virtually, thereby saving time for the dentist as well as allowing patients to interact from the comfort and safety of their homes. Saudi Arabia is a world leader in per capita Internet connectivity. There had been efforts to validate easy-to-use methods such as mobile phone teledentistry even before pandemic began. Recent national surveys have shown that over 70% of dentists have a positive view of teledentistry and over half of the dentists surveyed were using teledentistry in some form.[3,4] These studies have highlighted the need to develop national programs to educate both dentists and patients about teledentistry. While the benefits of such a program would benefit all patients and dentists, the benefit would perhaps be the greatest for those in the greatest need, individuals with special health-care needs.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".