The trustworthiness of results is the bedrock of high quality qualitative research. Member checking, also known as participant or respondent validation, is a technique for exploring the credibility of results. Data or results are returned to participants to check for accuracy and resonance with their experiences. Member checking is often mentioned as one in a list of validation techniques. This simplistic reporting might not acknowledge the value of using the method, nor its juxtaposition with the interpretative stance of qualitative research. In this commentary, we critique how member checking has been used in published research, before describing and evaluating an innovative in-depth member checking technique, Synthesized Member Checking. The method was used in a study with patients diagnosed with melanoma. Synthesized Member Checking addresses the co-constructed nature of knowledge by providing participants with the opportunity to engage with, and add to, interview and interpreted data, several months after their semi-structured interview.

Early diagnosis is a key factor in improving the outcomes of cancer patients. A greater understanding of the pre-diagnostic patient pathways is vital yet, at present, research in this field lacks consistent definitions and methods. As a consequence much early diagnosis research is difficult to interpret. A consensus group was formed with the aim of producing guidance and a checklist for early cancer-diagnosis researchers. A consensus conference approach combined with nominal group techniques was used. The work was supported by a systematic review of early diagnosis literature, focussing on existing instruments used to measure time points and intervals in early cancer-diagnosis research. A series of recommendations for definitions and methodological approaches is presented. This is complemented by a checklist that early diagnosis researchers can use when designing and conducting studies in this field. The Aarhus checklist is a resource for early cancer-diagnosis research that should promote greater precision and transparency in both definitions and methods. Further work will examine whether the checklist can be readily adopted by researchers, and feedback on the guidance will be used in future updates.

Purpose Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs). Methods BOADICEA incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information. Results Among all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of women predicted to have a lifetime risk of ≥17–<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of ≥30% (high risk). Conclusion This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening.

The nature of cancer control is changing, with an increasing emphasis, fuelled by public and political demand, on prevention, early diagnosis, and patient experience during and after treatment. At the same time, primary care is increasingly promoted, by governments and health funders worldwide, as the preferred setting for most health care for reasons of increasing need, to stabilise health-care costs, and to accommodate patient preference for care close to home. It is timely, then, to consider how this expanding role for primary care can work for cancer control, which has long been dominated by highly technical interventions centred on treatment, and in which the contribution of primary care has been largely perceived as marginal. In this Commission, expert opinion from primary care and public health professionals with academic and clinical cancer expertise—from epidemiologists, psychologists, policy makers, and cancer specialists—has contributed to a detailed consideration of the evidence for cancer control provided in primary care and community care settings. Ranging from primary prevention to end-of-life care, the scope for new models of care is explored, and the actions needed to effect change are outlined. The strengths of primary care—its continuous, coordinated, and comprehensive care for individuals and families—are particularly evident in prevention and diagnosis, in shared follow-up and survivorship care, and in end-of-life care. A strong theme of integration of care runs throughout, and its elements (clinical, vertical, and functional) and the tools needed for integrated working are described in detail. All of this change, as it evolves, will need to be underpinned by new research and by continuing and shared multiprofessional development.

Objective: Patient pathways to presentation to health care professionals and initial management in primary care are key determinants of outcomes in cancer. Reducing diagnostic delays may result in improved prognosis and increase the proportion of early stage cancers identified. Investigating diagnostic delay could be facilitated by use of a robust theoretical framework. We systematically reviewed the literature reporting the application of Andersen's Model of Total Patient Delay (delay stages: appraisal, illness, behavioural, scheduling, treatment) in studies which assess cancer diagnosis. Methods: We searched four electronic databases and conducted a narrative synthesis. Inclusion criteria were studies which: reported primary research, focused on cancer diagnosis and explicitly applied one or more stages of the Andersen Model in the collection or analysis of data. Results: The vast majority of studies of diagnostic delay in cancer have not applied a theoretical model to inform data collection or reporting. Ten papers (reporting eight studies) met our inclusion criteria: three studied several cancers. The studies were heterogeneous in their methods and quality. The review confirmed that there are clearly identifiable stages between the recognition of a symptom, first presentation to a health care professional, subsequent diagnosis and initiation of treatment. There was strong evidence to support the existence and importance of appraisal and treatment delay as defined in the Andersen Model, although treatment delay requires expansion. There was some evidence to support scheduling delay which may be contributed to by both patient and the health service. Illness delay was often difficult to distinguish from appraisal delay. It was less clear whether behavioural delay exists as a separate significant stage. Conclusions: Greater consistency is required in the conduct and reporting of studies of diagnostic delay in cancer. We propose refinements to the Andersen Model which could be used to increase its validity and improve the consistency of reporting in future studies.