Professionals' views on providing personalized recurrence risks for de novo mutations: Implications for genetic counseling.

When an apparent de novo (new) genetic change has been identified as the cause of a serious genetic condition in a child, many couples would like to know the risk of this happening again in a future pregnancy. Current practice provides families with a population average risk of 1%-2%. However, this figure is not accurate for any specific couple, and yet, they are asked to make decisions about having another child and/or whether to have prenatal testing. The PREcision Genetic Counseling And REproduction (PREGCARE) study is a new personalized assessment strategy that refines a couple's recurrence risk prior to a new pregnancy, by analyzing several samples from the parent-child trio (blood, saliva, swabs, and father's sperm) using deep sequencing and haplotyping. Overall, this approach can reassure ~2/3 of couples who have a negligible (<0.1%) recurrence risk and focus support on those at higher risk (i.e. when mosaicism is identified in one of the parents). Here we present a qualitative interview study with UK clinical genetics professionals (n = 20), which investigate the potential implications of introducing such a strategy in genetics clinics. While thematic analysis of the interviews indicated perceived clinical utility, it also indicates a need to prepare couples for the psychosocial implications of parent-of-origin information and to support their understanding of the assessment being offered. When dealing with personalized reproductive risk, a traditional non-directive approach may not meet the needs of practitioner and client(s) and shared decision-making provides an additional framework that may relieve some patient burden. Further qualitative investigation with couples is planned.

Ethical preparedness in genomic medicine: how NHS clinical scientists navigate ethical issues.

Much has been published about the ethical issues encountered by clinicians in genetics/genomics, but those experienced by clinical laboratory scientists are less well described. Clinical laboratory scientists now frequently face navigating ethical problems in their work, but how they should be best supported to do this is underexplored. This lack of attention is also reflected in the ethics tools available to clinical laboratory scientists such as guidance and deliberative ethics forums, developed primarily to manage issues arising within the clinic.We explore what ethical issues are being experienced by clinical scientists, how they think such issues could be best analysed and managed, and whether their practice might be enhanced by more situated approaches to ethics deliberation and practice such as ethical preparedness. From thematic analysis of cases presented by clinical scientists at a specially convened meeting of the UK Genethics Forum, we derived three main ethical themes: (1) the redistribution of labour and responsibilities resulting from the practice of genomic medicine; (2) the interpretation and certainty of results and (3) the proposal that better standardisation and consistency of ethical approaches (for example, more guidelines and policy) could resolve some of the challenges arising.We argue that although standardisation is important for promoting shared understandings of good (including ethical) practice, supplementary approaches to enhance and sustain ethical preparedness will be important to help clinical scientists and others in the recently expanded genetic/genomic medicine environment foster quality ethical thinking.

Democratising or disrupting diagnosis? Ethical issues raised by the use of AI tools for rare disease diagnosis.

Computational phenotyping (CP) technology uses facial recognition algorithms to classify and potentially diagnose rare genetic disorders on the basis of digitised facial images. This AI technology has a number of research as well as clinical applications, such as supporting diagnostic decision-making. Using the example of CP, we examine stakeholders' views of the benefits and costs of using AI as a diagnostic tool within the clinic. Through a series of in-depth interviews (n â€‹= â€‹20) with: clinicians, clinical researchers, data scientists, industry and support group representatives, we report stakeholder views regarding the adoption of this technology in a clinical setting. While most interviewees were supportive of employing CP as a diagnostic tool in some capacity we observed ambivalence around the potential for artificial intelligence to overcome diagnostic uncertainty in a clinical context. Thus, while there was widespread agreement amongst interviewees concerning the public benefits of AI assisted diagnosis, namely, its potential to increase diagnostic yield and enable faster more objective and accurate diagnoses by up skilling non specialists and thereby enabling access to diagnosis that is potentially lacking, interviewees also raised concerns about ensuring algorithmic reliability, expunging algorithmic bias and that the use of AI could result in deskilling the specialist clinical workforce. We conclude that, prior to widespread clinical implementation, on-going reflection is needed regarding the trade-offs required to determine acceptable levels of bias and conclude that diagnostic AI tools should only be employed as an assistive technology within the dysmorphology clinic.

Artificial intelligence and medical research databases: ethical review by data access committees.

BACKGROUND: It has been argued that ethics review committees-e.g., Research Ethics Committees, Institutional Review Boards, etc.- have weaknesses in reviewing big data and artificial intelligence research. For instance, they may, due to the novelty of the area, lack the relevant expertise for judging collective risks and benefits of such research, or they may exempt it from review in instances involving de-identified data. MAIN BODY: Focusing on the example of medical research databases we highlight here ethical issues around de-identified data sharing which motivate the need for review where oversight by ethics committees is weak. Though some argue for ethics committee reform to overcome these weaknesses, it is unclear whether or when that will happen. Hence, we argue that ethical review can be done by data access committees, since they have de facto purview of big data and artificial intelligence projects, relevant technical expertise and governance knowledge, and already take on some functions of ethical review. That said, like ethics committees, they may have functional weaknesses in their review capabilities. To strengthen that function, data access committees must think clearly about the kinds of ethical expertise, both professional and lay, that they draw upon to support their work. CONCLUSION: Data access committees can undertake ethical review of medical research databases provided they enhance that review function through professional and lay ethical expertise.

The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions.

Artificial intelligence (AI) is often cited as a possible solution to current issues faced by healthcare systems. This includes the freeing up of time for doctors and facilitating person-centred doctor-patient relationships. However, given the novelty of artificial intelligence tools, there is very little concrete evidence on their impact on the doctor-patient relationship or on how to ensure that they are implemented in a way which is beneficial for person-centred care.Given the importance of empathy and compassion in the practice of person-centred care, we conducted a literature review to explore how AI impacts these two values. Besides empathy and compassion, shared decision-making, and trust relationships emerged as key values in the reviewed papers. We identified two concrete ways which can help ensure that the use of AI tools have a positive impact on person-centred doctor-patient relationships. These are (1) using AI tools in an assistive role and (2) adapting medical education. The study suggests that we need to take intentional steps in order to ensure that the deployment of AI tools in healthcare has a positive impact on person-centred doctor-patient relationships. We argue that the proposed solutions are contingent upon clarifying the values underlying future healthcare systems.

A practical checklist for return of results from genomic research in the European context.

An increasing number of European research projects return, or plan to return, individual genomic research results (IRR) to participants. While data access is a data subject's right under the General Data Protection Regulation (GDPR), and many legal and ethical guidelines allow or require participants to receive personal data generated in research, the practice of returning results is not straightforward and raises several practical and ethical issues. Existing guidelines focusing on return of IRR are mostly project-specific, only discuss which results to return, or were developed outside Europe. To address this gap, we analysed existing normative documents identified online using inductive content analysis. We used this analysis to develop a checklist of steps to assist European researchers considering whether to return IRR to participants. We then sought feedback on the checklist from an interdisciplinary panel of European experts (clinicians, clinical researchers, population-based researchers, biobank managers, ethicists, lawyers and policy makers) to refine the checklist. The checklist outlines seven major components researchers should consider when determining whether, and how, to return results to adult research participants: 1) Decide which results to return; 2) Develop a plan for return of results; 3) Obtain participant informed consent; 4) Collect and analyse data; 5) Confirm results; 6) Disclose research results; 7) Follow-up and monitor. Our checklist provides a clear outline of the steps European researchers can follow to develop ethical and sustainable result return pathways within their own research projects. Further legal analysis is required to ensure this checklist complies with relevant domestic laws.

Providing recurrence risk counselling for parents after diagnosis of a serious genetic condition caused by an apparently de novo mutation in their child: a qualitative investigation of the PREGCARE strategy with UK clinical genetics practitioners.

BACKGROUND: Diagnosis of a child with a genetic condition leads to parents asking whether there is a risk the condition could occur again with future pregnancies. If the cause is identified as an apparent de novo mutation (DNM), couples are currently given a generic, population average, recurrence risk of ~1%-2%, depending on the condition. Although DNMs usually arise as one-off events, they can also originate through the process of mosaicism in either parent; in this instance, the DNM is present in multiple germ cells and the actual recurrence risk could theoretically be as high as 50%. METHODS: Our qualitative interview study examined the views and reflections on current practice provided by UK practitioners working in clinical genetics (n=20) regarding the potential impact of PREcision Genetic Counselling And REproduction (PREGCARE)-a new preconception personalised recurrence risk assessment strategy. RESULTS: Those interviewed regarded PREGCARE as a very useful addition to risk management, especially for cases where it revised the risk downwards or clarified that a couple's personalised recurrence risk meets National Health Service thresholds for non-invasive prenatal testing, otherwise inaccessible based on the generic DNM recurrence risk. CONCLUSION: Participants said it could release some couples requiring reassurance from undergoing unnecessary invasive testing in future pregnancies. However, they regarded mosaicism and PREGCARE as complex concepts to communicate, requiring further training and additional appointment time for pre-test genetic counselling to prepare couples for all the possible outcomes of a personalised risk assessment, including potentially identifying the parental origin of the DNM, and to ensure informed consent.

Germline mismatch repair (MMR) gene analyses from English NHS regional molecular genomics laboratories 1996-2020: development of a national resource of patient-level genomics laboratory records.

OBJECTIVE: To describe national patterns of National Health Service (NHS) analysis of mismatch repair (MMR) genes in England using individual-level data submitted to the National Disease Registration Service (NDRS) by the NHS regional molecular genetics laboratories. DESIGN: Laboratories submitted individual-level patient data to NDRS against a prescribed data model, including (1) patient identifiers, (2) test episode data, (3) per-gene results and (4) detected sequence variants. Individualised per-laboratory algorithms were designed and applied in NDRS to extract and map the data to the common data model. Laboratory-level MMR activity audit data from the Clinical Molecular Genetics Society/Association of Clinical Genomic Science were used to assess early years' missing data. RESULTS: Individual-level data from patients undergoing NHS MMR germline genetic testing were submitted from all 13 English laboratories performing MMR analyses, comprising in total 16 722 patients (9649 full-gene, 7073 targeted), with the earliest submission from 2000. The NDRS dataset is estimated to comprise >60% of NHS MMR analyses performed since inception of NHS MMR analysis, with complete national data for full-gene analyses for 2016 onwards. Out of 9649 full-gene tests, 2724 had an abnormal result, approximately 70% of which were (likely) pathogenic. Data linkage to the National Cancer Registry demonstrated colorectal cancer was the most frequent cancer type in which full-gene analysis was performed. CONCLUSION: The NDRS MMR dataset is a unique national pan-laboratory amalgamation of individual-level clinical and genomic patient data with pseudonymised identifiers enabling linkage to other national datasets. This growing resource will enable longitudinal research and can form the basis of a live national genomic disease registry.

"I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease.

BACKGROUND: As the use of AI becomes more pervasive, and computerised systems are used in clinical decision-making, the role of trust in, and the trustworthiness of, AI tools will need to be addressed. Using the case of computational phenotyping to support the diagnosis of rare disease in dysmorphology, this paper explores under what conditions we could place trust in medical AI tools, which employ machine learning. METHODS: Semi-structured qualitative interviews (n = 20) with stakeholders (clinical geneticists, data scientists, bioinformaticians, industry and patient support group spokespersons) who design and/or work with computational phenotyping (CP) systems. The method of constant comparison was used to analyse the interview data. RESULTS: Interviewees emphasized the importance of establishing trust in the use of CP technology in identifying rare diseases. Trust was formulated in two interrelated ways in these data. First, interviewees talked about the importance of using CP tools within the context of a trust relationship; arguing that patients will need to trust clinicians who use AI tools and that clinicians will need to trust AI developers, if they are to adopt this technology. Second, they described a need to establish trust in the technology itself, or in the knowledge it provides-epistemic trust. Interviewees suggested CP tools used for the diagnosis of rare diseases might be perceived as more trustworthy if the user is able to vouchsafe for the technology's reliability and accuracy and the person using/developing them is trusted. CONCLUSION: This study suggests we need to take deliberate and meticulous steps to design reliable or confidence-worthy AI systems for use in healthcare. In addition, we need to devise reliable or confidence-worthy processes that would give rise to reliable systems; these could take the form of RCTs and/or systems of accountability transparency and responsibility that would signify the epistemic trustworthiness of these tools. words 294.

Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model.

There is a growing consensus among scholars, national governments, and intergovernmental organisations of the need to involve the public in decision-making around the use of artificial intelligence (AI) in society. Focusing on the UK, this paper asks how that can be achieved for medical AI research, that is, for research involving the training of AI on data from medical research databases. Public governance of medical AI research in the UK is generally achieved in three ways, namely, via lay representation on data access committees, through patient and public involvement groups, and by means of various deliberative democratic projects such as citizens' juries, citizen panels, citizen assemblies, etc.-what we collectively call "citizen forums". As we will show, each of these public involvement initiatives have complementary strengths and weaknesses for providing oversight of medical AI research. As they are currently utilized, however, they are unable to realize the full potential of their complementarity due to insufficient information transfer across them. In order to synergistically build on their contributions, we offer here a multi-scale model integrating all three. In doing so we provide a unified public governance model for medical AI research, one that, we argue, could improve the trustworthiness of big data and AI related medical research in the future.

How might the public be authentically involved in decisions about medical data sharing for artificial intelligence (AI) research? In this paper, we highlight three ways in which public views are used to improve such decisions, namely, through lay representation on data access committees, through patient and public involvement groups, and through a variety of public engagement events we call "citizen forums." Though each approach has common strengths and weaknesses, we argue that they are unable to support each other due to a lack of proper integration. We therefore propose combining them so that they work in a more coordinated way. The combined model, we argue, could be useful for improving the trustworthiness of big data and AI related medical research in the future.

The ethical challenges of artificial intelligence-driven digital pathology.

Digital pathology - the digitalisation of clinical histopathology services through the scanning and storage of pathology slides - has opened up new possibilities for health care in recent years, particularly in the opportunities it brings for artificial intelligence (AI)-driven research. Recognising, however, that there is little scholarly debate on the ethics of digital pathology when used for AI research, this paper summarises what it sees as four key ethical issues to consider when deploying AI infrastructures in pathology, namely, privacy, choice, equity, and trust. The themes are inspired from the authors' experience grappling with the challenge of deploying an ethical digital pathology infrastructure to support AI research as part of the National Pathology Imaging Cooperative (NPIC), a collaborative of universities, hospital trusts, and industry partners largely located across the North of England. Though focusing on the UK case, internationally, few pathology departments have gone fully digital, and so the themes developed here offer a heuristic for ethical reflection for other departments currently making a similar transition or planning to do so in the future. We conclude by promoting the need for robust public governance mechanisms in AI-driven digital pathology.

Understanding the ethical and legal considerations of Digital Pathology.

Digital Pathology (DP) is a platform which has the potential to develop a truly integrated and global pathology community. The generation of DP data at scale creates novel challenges for the histopathology community in managing, processing, and governing the use of these data. The current understanding of, and confidence in, the legal and ethical aspects of DP by pathologists is unknown. We developed an electronic survey (e-survey), comprising 22 questions, with input from the Royal College of Pathologists (RCPath) Digital Pathology Working Group. The e-survey was circulated via e-mail and social media (Twitter) through the RCPath Digital Pathology Working Group network, RCPath Trainee Committee network, the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) digital pathology consortium, National Pathology Imaging Co-operative (NPIC), local contacts, and to the membership of both The Pathological Society of Great Britain and Ireland and the British Division of the International Academy of Pathology (BDIAP). Between 14 July 2020 and 6 September 2020, we collected 198 responses representing a cross section of histopathologists, including individuals with experience of DP research. We ascertained that, in the UK, DP is being used for diagnosis, research, and teaching, and that the platform is enabling data sharing. Our survey demonstrated that there is often a lack of confidence and understanding of the key issues of consent, legislation, and ethical guidelines. Of 198 respondents, 82 (41%) did not know when the use of digital scanned slide images would fall under the relevant legislation and 93 (47%) were 'Not confident at all' in their interpretation of consent for scanned slide images in research. With increasing uptake of DP, a working knowledge of these areas is essential but histopathologists often express a lack of confidence in these topics. The need for specific training in these areas is highlighted by the findings of this study.

A Just Standard: The Ethical Management of Incidental Findings in Brain Imaging Research.

Neuroimaging research regularly yields "incidental findings": observations of potential clinical significance in healthy volunteers or patients, but which are unrelated to the purpose or variables of the study.

Feasibility and design of a trial regarding the optimal mode of delivery for preterm birth: the CASSAVA multiple methods study.

BACKGROUND: Around 60,000 babies are born preterm (prior to 37 weeks' gestation) each year in the UK. There is little evidence on the optimal birth mode (vaginal or caesarean section). OBJECTIVE: The overall aim of the CASSAVA project was to determine if a trial to define the optimal mode of preterm birth could be carried out and, if so, determine what sort of trial could be conducted and how it could best be performed. We aimed to determine the specific groups of preterm women and babies for whom there are uncertainties about the best planned mode of birth, and if there would be willingness to recruit to, and participate in, a randomised trial to address some, but not all, of these uncertainties. This project was conducted in response to a Heath Technology Assessment programme commissioning call (17/22 'Mode of delivery for preterm infants'). METHODS: We conducted clinician and patient surveys (n = 224 and n = 379, respectively) to identify current practice and opinion, and a consensus survey and Delphi workshop (n = 76 and n = 22 participants, respectively) to inform the design of a hypothetical clinical trial. The protocol for this clinical trial/vignette was used in telephone interviews with clinicians (n = 24) and in focus groups with potential participants (n = 13). RESULTS: Planned sample size and data saturation was achieved for all groups except for focus groups with participants, as this had to be curtailed because of the COVID-19 pandemic and data saturation was not achieved. There was broad agreement from parents and health-care professionals that a trial is needed. The clinician survey demonstrated a variety of practice and opinion. The parent survey suggested that women and their families generally preferred vaginal birth at later gestations and caesarean section for preterm infants. The interactive workshop and Delphi consensus process confirmed the need for more evidence (hence the case for a trial) and provided rich information on what a future trial should entail. It was agreed that any trial should address the areas with most uncertainty, including the management of women at 26-32 weeks' gestation, with either spontaneous preterm labour (cephalic presentation) or where preterm birth was medically indicated. Clear themes around the challenges inherent in conducting any trial emerged, including the concept of equipoise itself. Specific issues were as follows: different clinicians and participants would be in equipoise for each clinical scenario, effective conduct of the trial would require appropriate resources and expertise within the hospital conducting the trial, potential participants would welcome information on the trial well before the onset of labour and minority ethnic groups would require tailored approaches. CONCLUSION: Given the lack of evidence and the variation of practice and opinion in this area, and having listened to clinicians and potential participants, we conclude that a trial should be conducted and the outlined challenges resolved. FUTURE WORK: The CASSAVA project could be used to inform the design of a randomised trial and indicates how such a trial could be carried out. Any future trial would benefit from a pilot with qualitative input and a study within a trial to inform optimal recruitment. LIMITATIONS: Certainty that a trial could be conducted can be determined only when it is attempted. TRIAL REGISTRATION: Current Controlled Trials ISRCTN12295730. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 61. See the NIHR Journals Library website for further project information.

Around 60,000 babies are born preterm each year in the UK. We do not know what the safest mode of birth is for these babies. Birth options include a vaginal birth or a caesarean section (which involves an operation for the mother). Normally, the ideal way to find out what clinical options are best is to carry out a 'randomised trial' in which participants are allocated to a particular treatment group (in this case, vaginal birth or caesarean section) by chance. It is not clear if women who have their babies preterm would want to take part in such a trial or that the clinicians looking after the women would be happy to ask them to, as previous trials have failed to recruit sufficient participants. The purpose of the CASSAVA research project was to find out what people think is the best and safest method of delivering preterm babies, their views on doing a research trial and what sort of research trial could be carried out. We conducted a survey asking clinicians and women their views. We gathered clinicians and women together to discuss and agree the key questions for a trial to answer. We then developed a protocol (plan) for a possible trial. Using this trial protocol, we conducted telephone interviews with clinicians, asking them if they would be willing to be involved and if they would be willing to ask pregnant women to participate. We also conducted focus groups with women, using a vignette (storyboard) about a possible trial. We found that there is a lot of uncertainty about the best way for preterm babies to be born. Clinicians and women broadly agreed that it would be good to resolve this uncertainty through a trial. We were able to identify some areas of the greatest uncertainty where clinicians and women would consider participating in a study. We gained a lot of useful information about how we could best set up a trial and support clinicians and women to get involved.

Governing AI-Driven Health Research: Are IRBs Up to the Task?

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted in order to provide an effective mechanism of oversight for health-related AI research.

Taking it to the bank: the ethical management of individual findings arising in secondary research.

A rapidly growing proportion of health research uses 'secondary data': data used for purposes other than those for which it was originally collected. Do researchers using secondary data have an obligation to disclose individual research findings to participants? While the importance of this question has been duly recognised in the context of primary research (ie, where data are collected from participants directly), it remains largely unexamined in the context of research using secondary data. In this paper, we critically examine the arguments for a moral obligation to disclose individual research findings in the context of primary research, to determine if they can be applied to secondary research. We conclude that they cannot. We then propose that the nature of the relationship between researchers and participants is what gives rise to particular moral obligations, including the obligation to disclose individual results. We argue that the relationship between researchers and participants in secondary research does not generate an obligation to disclose. However, we also argue that the biobanks or data archives which collect and provide access to secondary data may have such an obligation, depending on the nature of the relationship they establish with participants.

Digital/computational phenotyping: What are the differences in the science and the ethics?

The concept of 'digital phenotyping' was originally developed by researchers in the mental health field, but it has travelled to other disciplines and areas. This commentary draws upon our experiences of working in two scientific projects that are based at the University of Oxford's Big Data Institute - The RADAR-AD project and The Minerva Initiative - which are developing algorithmic phenotyping technologies. We describe and analyse the concepts of digital biomarkers and computational phenotyping that underlie these projects, explain how they are linked to other research in digital phenotyping and compare and contrast some of their epistemological and ethical implications. In particular, we argue that the phenotyping paradigm in both projects is grounded on an assumption of 'objectivity' that is articulated in different ways depending on the role that is given to the computational/digital tools. Using the concept of 'affordance', we show how specific functionalities relate to potential uses and social implications of these technologies and argue that it is important to distinguish among them as the concept of digital phenotyping is increasingly being used with a variety of meanings.

COVID-19 and Contact Tracing Apps: Ethical Challenges for a Social Experiment on a Global Scale.

Mobile applications are increasingly regarded as important tools for an integrated strategy of infection containment in post-lockdown societies around the globe. This paper discusses a number of questions that should be addressed when assessing the ethical challenges of mobile applications for digital contact-tracing of COVID-19: Which safeguards should be designed in the technology? Who should access data? What is a legitimate role for "Big Tech" companies in the development and implementation of these systems? How should cultural and behavioural issues be accounted for in the design of these apps? Should use of these apps be compulsory? What does transparency and ethical oversight mean in this context? We demonstrate that responses to these questions are complex and contingent and argue that if digital contract-tracing is used, then it should be clear that this is on a trial basis and its use should be subject to independent monitoring and evaluation.

Clinician-researchers and custodians of scarce resources: a qualitative study of health professionals' views on barriers to the involvement of teenagers and young adults in cancer trials.

BACKGROUND: Equipoise and role conflict have been previously identified as important factors in professionals' engagement with trials, inducing behaviours which can impact on recruitment. We explored these phenomena as potential explanations for the low levels of involvement of teenagers and young adults (TYA) with cancer in clinical trials in oncology. METHODS: We report findings from interviews with 30 purposively sampled direct-care professionals involved in delivering cancer care and/or facilitating clinical trials in Scotland. We undertook qualitative descriptive analysis, focussed on identifying key issues and themes. RESULTS: Interviewees largely identified as clinician-researchers and portrayed oncology as a specialty in which research was integral to care. They saw their primary responsibility as ensuring patients received the best treatment, but asserted that, in general, trials provided a vehicle for optimal care. Role conflict in its traditional form was rarely evident; however, other tensions were manifest. Professionals found the significant time costs of delivering trials difficult to reconcile with the increasing pressures on clinical services. They felt a responsibility to make prudent choices about the trials with which to engage. Guided by utilitarian principles, these choices were oriented towards benefiting the largest number of patients. This favoured trials in high volume diseases; as TYA tend to have rarer forms of cancer, professionals' support for-and TYA's access to-relevant trials was, by default, more limited. CONCLUSIONS: Neither lack of individual equipoise nor experiences of traditional forms of role conflict accounted for the low levels of involvement of TYA with cancer in clinical trials. However, prominent tensions around the management of scarce resources provided an alternative explanation for TYA's limited access to cancer trials. The prevailing approach to decision-making about whether and which trials to support was recognised as contributing to inequalities in access and care. Professionals' choices, however, were made in the context of scarcity, and structured by incentives and sanctions understood by them as signalling governmental priorities. A franker discussion of the extent and distribution of the costs and benefits of trials work is needed, for change to be achieved.