Determinants of infant mortality and representation in bioarchaeological samples : a review

ACKNOWLEDGEMENTS This article is dedicated to the memory of Alistair D. E. Muir (1972 - 2020). This research was partly funded by a British Academy grant GP2\190224. The authors thank the reviewers for their feedback which has improved this manuscript.Peer reviewedPostprin

Estimating Fertility using Adults : A Method for Under-enumerated Pre-adult Skeletal Samples

ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their suggestions which contributed to the improvement of this article. This research was supported by an Australian Government Research Training Program Scholarship. Article Funding Open access publishing facilitated by Australian National University, as part of the Wiley - Australian National University agreement via the Council of Australian University Librarians.Peer reviewedPublisher PD

Temporal trends in the colonisation of the Pacific : Palaeodemographic insights

Open Access via the Springer Compact Agreement. This research was supported by an Australian Government Research Training Program (RTP) Scholarship, Australian Research Council; Grant number: FT 120100299; and Institute of Advanced Study (IAS), Durham University and The COFUND ‘Durham International Fellowships for Research and Enterprise’ scheme. We also thank Les O’Neill, Archaeology Programme, University of Otago, for creating Figure 1.Peer reviewedPublisher PD

Comparisons of age-at-death distributions among extinct hominins and extant nonhuman primates indicate normal mortality

Acknowledgements The authors wish to thank the anonymous reviewers and the editor for their constructive and helpful feedback, which has undoubtedly improved this manuscript.Peer reviewedPostprin

A comparative study of Norse palaeodemography in the North Atlantic

Acknowledgements British Academy Global Professorship GP2\190224. The authors would like to thank Ricky Craig, Independent GIS Specialist and Cartographer, Scotland, for preparing Figure 1, the map.Peer reviewe

Reporting radiographers’ interaction with Artificial Intelligence—How do different forms of AI feedback impact trust and decision switching?

Artificial Intelligence (AI) has been increasingly integrated into healthcare settings, including the radiology department to aid radiographic image interpretation, including reporting by radiographers. Trust has been cited as a barrier to effective clinical implementation of AI. Appropriating trust will be important in the future with AI to ensure the ethical use of these systems for the benefit of the patient, clinician and health services. Means of explainable AI, such as heatmaps have been proposed to increase AI transparency and trust by elucidating which parts of image the AI ‘focussed on’ when making its decision. The aim of this novel study was to quantify the impact of different forms of AI feedback on the expert clinicians’ trust. Whilst this study was conducted in the UK, it has potential international application and impact for AI interface design, either globally or in countries with similar cultural and/or economic status to the UK. A convolutional neural network was built for this study; trained, validated and tested on a publicly available dataset of MUsculoskeletal RAdiographs (MURA), with binary diagnoses and Gradient Class Activation Maps (GradCAM) as outputs. Reporting radiographers (n = 12) were recruited to this study from all four regions of the UK. Qualtrics was used to present each participant with a total of 18 complete examinations from the MURA test dataset (each examination contained more than one radiographic image). Participants were presented with the images first, images with heatmaps next and finally an AI binary diagnosis in a sequential order. Perception of trust in the AI systems was obtained following the presentation of each heatmap and binary feedback. The participants were asked to indicate whether they would change their mind (or decision switch) in response to the AI feedback. Participants disagreed with the AI heatmaps for the abnormal examinations 45.8% of the time and agreed with binary feedback on 86.7% of examinations (26/30 presentations).’Only two participants indicated that they would decision switch in response to all AI feedback (GradCAM and binary) (0.7%, n = 2) across all datasets. 22.2% (n = 32) of participants agreed with the localisation of pathology on the heatmap. The level of agreement with the GradCAM and binary diagnosis was found to be correlated with trust (GradCAM:—.515;—.584, significant large negative correlation at 0.01 level (p = < .01 and—.309;—.369, significant medium negative correlation at .01 level (p = < .01) for GradCAM and binary diagnosis respectively). This study shows that the extent of agreement with both AI binary diagnosis and heatmap is correlated with trust in AI for the participants in this study, where greater agreement with the form of AI feedback is associated with greater trust in AI, in particular in the heatmap form of AI feedback. Forms of explainable AI should be developed with cognisance of the need for precision and accuracy in localisation to promote appropriate trust in clinical end users

Ageing the elderly: A new approach to the estimation of the age-at-death distribution from skeletal remains

This study reports on the use of a proportional measure to estimate the age‐at‐death distribution of an assemblage and, when combined with a seriation method, additionally estimate the age‐at‐death of individuals. Traditional methods of estimating age‐at‐death suffer from a number of issues, including decreasing accuracy with increasing age, age mimicry of the reference population, and difficulty balancing accuracy with precision. A new method is proposed for estimating the age‐at‐death distribution of middle and older adults. As the age‐at‐death distribution is significantly impacted by the fertility rate, it was hypothesised that the D0‐14/D ratio (the number of individuals who died aged 0-14 years divided by the total population; an indicator of fertility) may be able to estimate the proportion of individuals that might be expected to die in each five‐year age group over 35 years. The method permits the estimation of individual age when used in conjunction with seriation methods and the age‐at‐death distribution of a population. The method is tested on two samples of known age, the Spitalfields crypt and St Thomas' Church cemetery collections and found to provide greater accuracy over previously applied methods

An evaluation of a checklist in Musculoskeletal (MSK) radiographic image interpretation when using Artificial Intelligence (AI)

Background: AI is being used increasingly in image interpretation tasks. There are challenges for its optimal use in reporting environments. Human reliance on technology and bias can cause decision errors. Trust issues exist amongst radiologists and radiographers in both over-reliance (automation bias) and reluctance in AI use for decision support. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene. Method: A checklist, to be used in image interpretation with AI assistance, was developed. Participants interpreted 20 examinations with AI assistance and then re- interpreted the 20 examinations with AI and a checklist. The MSK images were presented to radiographers as patient examinations to replicate the image interpretation task in clinical practice. Image diagnosis and confidence levels on the diagnosis provided were collected following each interpretation. The participant perception of the use of the checklist was investigated via a questionnaire.Results: Data collection and analysis are underway and will be completed at the European Congress of Radiology in Vienna, March 2023. The impact of the use of a checklist in image interpretation with AI will be evaluated. Changes in accuracy and confidence will be investigated and results will be presented. Participant feedback will be analysed to determine perceptions and impact of the checklist also. Conclusion: A novel checklist has been developed to aid the interpretation of images when using AI. The checklist has been tested for its use in assisting radiographers in MSK image interpretation when using AI.<br/