Australian dental journal最新文献

Purpose: The purpose of this in vitro study was to evaluate the influence of implant scan body material and its exposed portion on ti-base (titanium-based) positioning accuracy.

Material and methods: In this study, a total of 14 diagnostic casts were created using two different scan bodies in 7 subgroups, ranging from the bone level to a depth of 6 mm. The diagnostic casts were scanned using a desktop scanner (Ceramill Map 600+, Amann Girrbach, Austria) for reference. The experimental data were scanned five times with an intraoral scanner (TRIOS 5, 3Shape, Copenhagen, Denmark) in each group (n = 70). All scan data were transferred to CAD software (Exocad, DentalCAD 3.1 Rijeka, Darmstadt, Germany). The ti-base positions were determined after the scan body image matching. The ti-base position deviations were analysed in three dimensions. A two-way ANOVA test was used to compare data according to depth and material variables.

Results: The depth factor (p = 0.000) and the interaction between depth and material (p = 0.006) had statistically significant effects on the accuracy of ti-base positioning, whereas the material factor did not show a significant effect (p = 0.559).

Conclusions: As the exposure of the scan bodies decreased, the obtained deviation values tended to increase for both scan body materials.

Publishing a journal is all about impact. Impact is defined by the Oxford English Dictionary as (1) the action of one object coming forcibly into contact with another or (2) a marked effect or influence. Obviously, for a journal, (1) should not apply. However, impact is important for a journal, especially if it is supported by an organisation. Many national and international societies run their own journals and often through a publishing house. This costs money.

Journal impact is defined by a number of metrics including journal impact factor, journal cite score and journal citation indicator, but what do these actually mean to the reader?

A Google search using the words “why does journal impact factor matter?” came up with the following artificial intelligence generated statement which sums up quite nicely. “Journal Impact Factor (JIF) matters as a benchmark for a journal's influence, quality, and importance within its field, affecting researchers' decisions on where to publish and influencing career advancement, promotions, and grant applications.” JIF is the traditional metric and all journals are ranked using it; the higher the better. JIF may be an indicator of journal quality, that is, what you read is important and/or of high scientific merit. It is be used by authors to decide where to publish. For example, the Australian Dental Journal receives over 800 submissions a year. Some academic institutions will look at JIF for academic promotions. Lastly, it may indicate if a journal should continue to be published. However, JIF has limitations as it reflects the journal overall and not individual papers, may be skewed by high citations of one paper, and is generally specific to just one field, such as dentistry. Hence the introduction of other metrics.

Journal CiteScore is stated by Wiley publishing house as “The CiteScore is calculated by dividing the number of citations to documents published in a 4-year period by the number of documents in same 4-year period.” Included in calculating its value are citations from articles, reviews, conference presentations, and book chapters, all of which really reflect the quality of the journal papers.

Lastly, the journal citation indicator looks at how the journal compares across different fields of research, not just dentistry, taking into account how many editions a year and how many citations. It is meant to allow easy comparison of a journal across disciplines.

These two measures are meant to complement and remove some of the issues with JIF. However, JIF is still the most commonly used. The 2024 JIF for the Australian Dental Journal is 2.4, the highest ever and places the journal in the top 25% of dental journals worldwide. There has been a conscious effort to promote Australian and New Zealand research, and it is pleasing to see that the quality of our research is recognised.

The author declares no conflicts of interest.

Objectives: To assess the oral health status of patients with severe mental illness visiting the Collaborative Centre for Cardiometabolic Health in Psychosis clinics and examine the effect of demographic, medical and social factors on Oral Health (OH).

Methods: The cross-sectional study included adults aged 18-65 years with severe mental illness who attended the clinics in Sydney, Australia between June 2016 and December 2020. As part of the OH assessment, information about their oral hygiene behaviours was recorded and participants underwent dental examination to assess their dental status.

Results: The majority of the 845 patients were male (63%), with a mean age of 43.4 years and a diagnosis of Schizophrenia (61%). Co-morbidities included dyslipidaemia (93.3%), overweight/obesity (81%), and hypertension (47%). Gingival inflammation was noticed in 80% of participants. Caries experience was high, with 44% having active tooth decay requiring restoration or extraction. Age, smoking, schizophrenia, depression and cardiometabolic conditions like diabetes and hypertension were significantly associated with caries. Gingival inflammation was significantly associated with psychiatric diagnosis and antipsychotic medications, male gender, smoking and diabetes.

Conclusions: People with severe mental illness have a high prevalence of dental caries and gingival inflammation. A strong association between dental diseases and demographics as well as cardiometabolic conditions emphasises the need for a multipronged approach to improve the overall health outcomes of people with severe mental illness.

Objective: This research examines the extent of clinical variation across the NSW public dental service, the largest in Australia. Findings may assist policy makers in reducing variation of care and inequity towards value-based oral healthcare.

Methods: Four years of demographic and treatment data, for 741,450 patients between 1 July 2019 and 30 June 2023, were extracted from the Electronic Oral Health Record System and analysed. Sixteen common dental treatment types provided by NSW Local Health Districts (LHDs) for adults and children were analysed relative to all treated patients in terms of clinical variation. Adult-only treatment types included posterior root canal treatment, fixed crowns, occlusal splints and metal-based partial dentures, removal of calculus, as well as full dentures and acrylic partial dentures. Also included, were simple fillings and simple extractions for both adults and children, as well as preformed crowns, pulpotomy, sport mouthguards, treatment under nitrous oxide gas sedation, fissure sealing and fluoride application for children.

Results: Of the 16 total treatment types investigated, eight showed significant variations ranging between 43-961 fold for adults and 17-434 fold for children. In contrast, eight treatments showed far less variation ranging between 1.5 and 2.8 fold.

Conclusion: This research identified significant variations in general dental adult and child treatments provided across NSW LHDs which cannot be attributed or explained demographically. The presence of unwarranted clinical variation would suggest disproportionate and inequitable care delivery across NSW public dental services. Addressing unwarranted clinical variation will likely promote fairer and more equitable provision of public oral health with enhanced patient outcomes.

This review contextualises the role of generative Artificial Intelligence (AI) in healthcare within an Australian healthcare regulatory and ethical framework. Four online databases (PubMed, Scopus, CINAHL and Web of Science) were searched for peer-reviewed publications that addressed at least two of the three topics: (1) the current applications of AI in dentistry and healthcare; (2) data security and privacy in AI-enhanced healthcare; (3) ethical, legal and clinical implications of machine errors, and the delegation of healthcare responsibilities to large technology companies. A total of 31 articles were retrieved for full-text analysis using traditional and AI-assisted software. All studies showed promising use of AI to enhance clinical decision-making, automate administrative tasks and augment personalised care. However, integrating AI into the Australian healthcare context introduces complex ethical, regulatory and legal considerations that include bias, data privacy and ambiguous chains of responsibility. To maximise the benefits of AI technologies while safeguarding patient rights, practitioners and developers must establish regulatory frameworks, mandate practitioner training, foster multidisciplinary collaboration and maintain continuous rigorous oversight.