Clinical trial adaptation by matching evidence in complementary patient sub-groups of auxiliary blinding questionnaire responses

Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial. Such adjustment may take on various forms, including the change in the dose of administered medicines, the frequency of administering an intervention, the number of trial participants, or the duration of the trial, to name just some possibilities. The main goal is to make the process of introducing new medical interventions to patients more efficient, either by reducing the cost or the time associated with evaluating their safety and efficacy. The principal challenge, which is an outstanding research problem, is to be found in the question of how adaptation should be performed so as to minimize the chance of distorting the outcome of the trial. In this paper we propose a novel method for achieving this. Unlike most of the previously published work, our approach focuses on trial adaptation by sample size adjustment i.e. by reducing the number of trial participants in a statistically informed manner. We adopt a stratification framework recently proposed for the analysis of trial outcomes in the presence of imperfect blinding and based on the administration of a generic auxiliary questionnaire that allows the participants to express their belief concerning the assigned intervention (treatment or control). We show that this data, together with the primary measured variables, can be used to make the probabilistically optimal choice of the particular sub-group a participant should be removed from if trial size reduction is desired. Extensive experiments on a series of simulated trials are used to illustrate the effectiveness of our method

Crime and punishment : a rethink

Incarceration remains the foremost form of sentence for serious crimes in Western democracies. At the same time, the management of prisons and of the prison population has become a major real-world challenge, with growing concerns about overcrowding, the offenders’ well-being, and the failure of achieving the distal desideratum of reduced criminality, all of which have a moral dimension. In no small part motivated by these practical problems, the focus of the present article is on the ethical framework that we use in thinking about and administering criminal justice. I start with an analysis of imprisonment and its permissibility as a punitive tool of justice. In particular, I present a novel argument against punitive imprisonment, showing it to fall short in meeting two key criteria of just punishment, namely (i) that the appropriate individual is being punished, and (ii) that the punishment can be adequately moderated to reflect the seriousness of the crime. The principles I argue for and that the aforementioned analysis brings to the fore, rooted in the sentient experience, firstly of victims, and not only of victims but also of the offenders as well as the society at large, then lead me to elucidate the broader framework of jurisprudence that I then apply more widely. Hence, while rejecting punitive imprisonment, I use its identified shortcomings to argue for the reinstitution of forms of punishment that are, incongruently, presently not seen as permissible, such as corporal punishment and punishments dismissed on the basis of being seen as humiliating. I also present a novel view of capital punishment, which, in contradiction to its name, I reject for punitive aims, but which I argue is permissible on compassionate grounds.Publisher PDFPeer reviewe

COVID-19 and science communication : the recording and reporting of disease mortality

The ongoing COVID-19 pandemic has brought science to the fore of public discourse and, considering the complexity of the issues involved, with it also the challenge of effective and informative science communication. This is a particularly contentious topic, in that it is both highly emotional in and of itself; sits at the nexus of the decision-making process regarding the handling of the pandemic, which has effected lockdowns, social behaviour measures, business closures, and others; and concerns the recording and reporting of disease mortality. To clarify a point that has caused much controversy and anger in the public debate, the first part of the present article discusses the very fundamentals underlying the issue of causative attribution with regards to mortality, lays out the foundations of the statistical means of mortality estimation, and concretizes these by analysing the recording and reporting practices adopted in England and their widespread misrepresentations. The second part of the article is empirical in nature. I present data and an analysis of how COVID-19 mortality has been reported in the mainstream media in the UK and the USA, including a comparative analysis both across the two countries as well as across different media outlets. The findings clearly demonstrate a uniform and worrying lack of understanding of the relevant technical subject matter by the media in both countries. Of particular interest is the finding that with a remarkable regularity (ρ>0.998), the greater the number of articles a media outlet has published on COVID-19 mortality, the greater the proportion of its articles misrepresented the disease mortality figures.Publisher PDFPeer reviewe

Whole slide pathology image patch based deep classification : an investigation of the effects of the latent autoencoder representation and the loss function form

The analysis of whole-slide pathological images is a major area of deep learning applications in medicine. The automation of disease identification, prevention, diagnosis, and treatment selection from whole-slide images (WSIs) has seen many advances in the last decade due to the progress made in the areas of computer vision and machine learning. The focus of this work is on patch level to slide image level analysis of WSIs, popular in the existing literature. In particular, we investigate the nature of the information content present in images on the local level of individual patches using autoencoding. Driven by our findings at this stage, which raise questions about the us of autoencoders, we next address the challenge posed by what we argue is an overly coarse classification of patches as tumorous and non-tumorous, which leads to the loss of important information. We showed that task specific modifications of the loss function, which take into account the content of individual patches in a more nuanced manner, facilitate a dramatic reduction in the false negative classification rate.Postprin

A Case for 'Killer Robots': Why in the Long Run Martial AI May Be Good for Peace

Purpose: The remarkable increase of sophistication of artificial intelligence in recent years has already led to its widespread use in martial applications, the potential of so-called 'killer robots' ceasing to be a subject of fiction. Approach: Virtually without exception, this potential has generated fear, as evidenced by a mounting number of academic articles calling for the ban on the development and deployment of lethal autonomous robots (LARs). In the present paper I start with an analysis of the existing ethical objections to LARs. Findings: My analysis shows the contemporary thought to be deficient in philosophical rigour, these deficiencies leading to an alternative thesis

Big data driven detection of trees in suburban scenes using visual spectrum eye level photography

The aim of the work described in this paper is to detect trees in eye level view images. Unlike previous work that universally considers highly constrained environments, such as natural parks and wooded areas, or simple scenes with little clutter and clear tree separation, our focus is on much more challenging suburban scenes, which are rich in clutter and highly variable in type and appearance (houses, falls, shrubs, cars, bicycles, pedestrians, hydrants, lamp posts, etc.). Thus, we motivate and introduce three different approaches: (i) a conventional computer vision based approach, employing manually engineered steps and making use of explicit human knowledge of the application domain, (ii) a more machine learning oriented approach, which learns from densely extracted local features in the form of scale invariant features (SIFT), and (iii) a machine learning based approach, which employs both colour and appearance models as a means of making the most of available discriminative information. We also make a significant contribution in regards to the collection of training and evaluation data. In contrast to the existing work, which relies on manual data collection (thus risking unintended bias) or corpora constrained in variability and limited in size (thus not allowing for reliable generalisation inferences to be made), we show how large amounts of representative data can be collected automatically using freely available tools, such as Google’s Street View, and equally automatically processed to produce a large corpus of minimally biased imagery. Using a large data set collected in the manner and comprising tens of thousands of images, we confirm our theoretical arguments that motivated our machine learning based and colour-aware histograms of oriented gradients based method, which achieved a recall of 95% and precision of 97%.Publisher PDFPeer reviewe

Images of Roman Imperial denarii : a curated data set for the evaluation of computer vision algorithms applied to ancient numismatics, and an overview of challenges in the field

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin issues, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.Publisher PDFPeer reviewe

Resolving the Ethical Quagmire of the Persistent Vegetative State

A patient is diagnosed with the persistent vegetative state (PVS) when they show no evidence of the awareness of the self or the environment for an extended period of time. The chance of recovery of any mental function or the ability to interact in a meaningful way is low. Though rare, the condition, considering its nature as a state outwith the realm of the conscious, coupled with the trauma experienced by the patient's kin as well as health care staff confronted with painful decisions regarding the patient's care, has attracted a considerable amount of discussion within the bioethics community. At present, there is a wealth of literature that discusses the relevant neurology, that elucidates the plethora of ethical challenges in understanding and dealing with the condition, and that analyses the real-world cases which have prominently featured in the mainstream media as a result of emotionally charged, divergent views concerning the provision of care to the patient. However, there is scarcely anything in the published scholarly literature that proposes concrete and practically actionable solutions to the now widely recognized moral conundrums. The present article describes a step in that direction. I start from the very foundations, laying out a sentientist approach which serves as the basis for the consequent moral decision-making, and then proceed to systematically identify and deconstruct the different cases of discord, using the aforementioned foundations as the basis for their resolution. A major intellectual contribution concerns the fluidity of the duty of care which I argue is demanded by the sentientist focus. The said duty is shown initially to have for its object the patient, which depending on the circumstances, can change to the patient's kin, or the health care staff themselves. In conclusion, the proposed framework represents the first comprehensive proposal regarding the decision-making processes involved in the deliberation on the provision of life sustaining treatment to a patient in a PVS

Crime & Punishment: A Rethink

Incarceration remains the foremost form of sentence for serious crimes in Western democracies. At the same time, the management of prisons and of the prison population has become a major real-world challenge, with growing concerns about overcrowding, the offenders’ well-being, and the failure of achieving the distal desideratum of reduced criminality, all of which have a moral dimension. In no small part motivated by these practical problems, the focus of the present article is on the ethical framework that we use in thinking about and administering criminal justice. I start with an analysis of imprisonment and its permissibility as a punitive tool of justice. In particular, I present a novel argument against punitive imprisonment, showing it to fall short in meeting two key criteria of just punishment, namely (i) that the appropriate individual is being punished, and (ii) that the punishment can be adequately moderated to reflect the seriousness of the crime. The principles I argue for and that the aforementioned analysis brings to the fore, rooted in the sentient experience, firstly of victims, and not only of victims but also of the offenders as well as the society at large, then lead me to elucidate the broader framework of jurisprudence that I then apply more widely. Hence, while rejecting punitive imprisonment, I use its identified shortcomings to argue for the reinstitution of forms of punishment that are, incongruently, presently not seen as permissible, such as corporal punishment and punishments dismissed on the basis of being seen as humiliating. I also present a novel view of capital punishment, which, in contradiction to its name, I reject for punitive aims, but which I argue is permissible on compassionate grounds

NHS Underfunding and the Lopsided Socialized Model

Background: The funding of health care is a major challenge to governments all across the world; the UK presents a useful and illustrative case. Methodology: In this article I explain why the manner in which the provision of health care in the UK is organized is fundamentally incoherent and continuing to ignore this incoherence is bound to lead to ever-greater problems. Discussion: Our society must decide on its priorities; herein I do not wish to argue what these ought to be, but, rather more modestly, to emphasise the system's inconsistencies. Perspectives: A reorganization of the system is needed to ensure a modicum of consistency