John Peel's Home Truths

Although widely regarded as the most influential British radio DJ of the last century, John Peel was also a presenter of speech radio. This article examines his career presenting two BBC Radio 4 speech programmes; ‘Offspring’ and ‘Home Truths’. Research carried out included interviews with radio producers, close listening to selected programmes presented by Peel as well as reviewing the literature on radio presentation and broadcast talk. The evidence shows that Peel's success owed a lot to his ability to create a distinct radio persona, one which inspired a unique listening community. Themes of eccentricity, English nationalism, parenting and ageing were central to ‘Home Truths’ as were nostalgic references to the past. The article begins with a consideration of some of the relevant literature on radio presentation and DJs followed by an account of Peel's early career. His experience writing for ‘Radio Times’ is described and the influence this had on his selection to present on Radio 4. There are detailed accounts of the genesis of both ‘Offspring’ and its successor, ‘Home Truths’ which explain the roles of women producers in their creation. Peel's persona is discussed with reference to concepts of broadcast talk, discursive space and co-presence

COVID-19 and Dentistry

From MDPI via Jisc Publications RouterHistory: accepted 2021-06-17, pub-electronic 2021-06-21Publication status: PublishedDentistry is a healthcare profession requiring close contacts between the dental practitioner and the patient. In particular, many dental procedures generate aerosols and droplets which are proved to be the major transmission route for COVID-19

A survey of dentists? practice in the restoration of the shortened dental arch

Objectives: The aim of this survey was to determine how a sample of dentists working in general dental practice in the UK sought to restore the mouths of patients who had shortened dental arches (SDAs). Study design: A survey was conducted over a period of six months on four commercial dental laboratories. Cases of SDAs comprising the anterior teeth and 2 to 4 premolars were examined; dental prescriptions were scrutinized and a special data collection form was completed accordingly. Results: A total of 140 SDA cases were examined. Most of these cases were for mandibular SDAs (88.6%). Of the recorded SDA cases, 67.2% were restored by cobalt-chromium based removable partial dentures (RPD); 25.7% were extended by acrylic-resin based RPDs; implants were provided to restore 8 SDA cases (5.7%); and only two SDA cases (1.4%) were extended by cantilevered fixed bridges. Neither the gender of the patient ([Chi (2)= 4.19, p>0.05) nor the length of the SDA ([Chi (2) = 6.51, p>0.05) influenced the choice of prosthesis to be implemented. Conclusions: It would appear from the results of this survey that among the different restorative treatment options for SDA, the RPD was the most popular treatment option for dentists surveyed in this UK study. Extending the SDA by either an implant-supported prosthesis or cantilevered fixed bridges did not appear to be a popular choice of treatment

The ADEPT study: a comparative study of dentists' ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of AssistDent artificial intelligence software

From Springer Nature via Jisc Publications RouterHistory: received 2020-10-20, accepted 2020-12-16, registration 2021-01-01, pub-electronic 2021-10-22, pub-print 2021-10-22, online 2021-10-22Publication status: PublishedAbstract: Introduction Reversal of enamel-only proximal caries by non-invasive treatments is important in preventive dentistry. However, detecting such caries using bitewing radiography is difficult and the subtle patterns are often missed by dental practitioners. Aims To investigate whether the ability of dentists to detect enamel-only proximal caries is enhanced by the use of AssistDent artificial intelligence (AI) software. Materials and methods In the ADEPT (AssistDent Enamel-only Proximal caries assessmenT) study, 23 dentists were randomly divided into a control arm, without AI assistance, and an experimental arm, in which AI assistance provided on-screen prompts indicating potential enamel-only proximal caries. All participants analysed a set of 24 bitewings in which an expert panel had previously identified 65 enamel-only carious lesions and 241 healthy proximal surfaces. Results The control group found 44.3% of the caries, whereas the experimental group found 75.8%. The experimental group incorrectly identified caries in 14.6% of the healthy surfaces compared to 3.7% in the control group. The increase in sensitivity of 71% and decrease in specificity of 11% are statistically significant (p <0.01). Conclusions AssistDent AI software significantly improves dentists' ability to detect enamel-only proximal caries and could be considered as a tool to support preventive dentistry in general practice