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The term “biobanking” is often misapplied to any collection of human biological materials (biospecimens) regardless of requirements related to ethical and legal issues or the standardization of different processes involved in tissue collection. A proper definition of biobanks is large collections of biospecimens linked to relevant personal and health information (health records, family history, lifestyle, genetic information) that are held predominantly for use in health and medical research. In addition, the International Organization for Standardization, in illustrating the requirements for biobanking (ISO 20387:2018), stresses the concept of biobanks being legal entities driving the process of acquisition and storage together with some or all of the activities related to collection, preparation, preservation, testing, analysing and distributing defined biological material as well as related information and data. In this review article, we aim to discuss the basic principles of biobanking, spanning from definitions to classification systems, standardization processes and documents, sustainability and ethical and legal requirements. We also deal with emerging specimens that are currently being generated and shaping the so-called next-generation biobanking, and we provide pragmatic examples of cancer-associated biobanking by discussing the process behind the construction of a biobank and the infrastructures supporting the implementation of biobanking in scientific research.

John Ioannidis argues that problem base, context placement, information gain, pragmatism, patient centeredness, value for money, feasibility, and transparency define useful clinical research. He suggests most clinical research is not useful and reform is overdue.

Background A standard or consensus definition of a systematic review does not exist. Therefore, if there is no definition about a systematic review in secondary studies that analyse them or the definition is too broad, inappropriate studies might be included in such evidence synthesis. The aim of this study was to analyse the definition of a systematic review (SR) in health care literature, elements of the definitions that are used and to propose a starting point for an explicit and non-ambiguous SR definition. Methods We included overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks. We extracted the definitions of SRs, as well as the inclusion and exclusion criteria that could indicate which definition of a SR the authors used. We extracted individual elements of SR definitions, categorised and quantified them. Results Among the 535 analysed sources of information, 188 (35%) provided a definition of a SR. The most commonly used reference points for the definitions of SRs were Cochrane and the PRISMA statement. We found 188 different elements of SR definitions and divided them into 14 categories. The highest number of SR definition elements was found in categories related to searching ( N  = 51), analysis/synthesis ( N  = 23), overall methods ( N  = 22), quality/bias/appraisal/validity ( N  = 22) and aim/question ( N  = 13). The same five categories were also the most commonly used combination of categories in the SR definitions. Conclusion Currently used definitions of SRs are vague and ambiguous, often using terms such as clear, explicit and systematic, without further elaboration. In this manuscript we propose a more specific definition of a systematic review, with the ultimate aim of motivating the research community to establish a clear and unambiguous definition of this type of research.

Citation analysis has become an important tool for research performance assessment in the medical sciences. However, different areas of medical research may have considerably different citation practices, even within the same medical field. Because of this, it is unclear to what extent citation-based bibliometric indicators allow for valid comparisons between research units active in different areas of medical research. A visualization methodology is introduced that reveals differences in citation practices between medical research areas. The methodology extracts terms from the titles and abstracts of a large collection of publications and uses these terms to visualize the structure of a medical field and to indicate how research areas within this field differ from each other in their average citation impact. Visualizations are provided for 32 medical fields, defined based on journal subject categories in the Web of Science database. The analysis focuses on three fields: Cardiac & cardiovascular systems, Clinical neurology, and Surgery. In each of these fields, there turn out to be large differences in citation practices between research areas. Low-impact research areas tend to focus on clinical intervention research, while high-impact research areas are often more oriented on basic and diagnostic research. Popular bibliometric indicators, such as the h-index and the impact factor, do not correct for differences in citation practices between medical fields. These indicators therefore cannot be used to make accurate between-field comparisons. More sophisticated bibliometric indicators do correct for field differences but still fail to take into account within-field heterogeneity in citation practices. As a consequence, the citation impact of clinical intervention research may be substantially underestimated in comparison with basic and diagnostic research.

Background Current methods for assessing strength of evidence prioritize the contributions of randomized controlled trials (RCTs). The objective of this study was to characterize strength of evidence (SOE) tools in recent use, identify their application to lifestyle interventions for improved longevity, vitality, or successful aging, and to assess implications of the findings. Methods The search strategy was created in PubMed and modified as needed for four additional databases: Embase, AnthropologyPlus, PsycINFO, and Ageline, supplemented by manual searching. Systematic reviews and meta-analyses of intervention trials or observational studies relevant to lifestyle intervention were included if they used a specified SOE tool. Data was collected for each SOE tool. Conditions necessary for assigning the highest SOE grading and treatment of prospective cohort studies within each SOE rating framework were summarized. The expert panel convened to discuss the implications of findings for assessing evidence in the domain of lifestyle medicine. Results and conclusions A total of 15 unique tools were identified. Ten were tools developed and used by governmental agencies or other equivalent professional bodies and were applicable in a variety of settings. Of these 10, four require consistent results from RCTs of high quality to award the highest rating of evidence. Most SOE tools include prospective cohort studies only to note their secondary contribution to overall SOE as compared to RCTs. We developed a new construct, Hierarchies of Evidence Applied to Lifestyle Medicine (HEALM), to illustrate the feasibility of a tool based on the specific contributions of diverse research methods to understanding lifetime effects of health behaviors. Assessment of evidence relevant to lifestyle medicine requires a potential adaptation of SOE approaches when outcomes and/or exposures obviate exclusive or preferential reliance on RCTs. This systematic review was registered with the International Prospective Register of Systematic Reviews, PROSPERO [CRD42018082148].

Letter to the Editor

Countries, research institutions, and scholars are interested in identifying and promoting high-impact and transformative scientific research. This paper presents a novel set of text- and citation-based metrics that can be used to identify high-impact and transformative works. The 11 metrics can be grouped into seven types: Radical-Generative, Radical-Destructive, Risky, Multidisciplinary, Wide Impact, Growing Impact, and Impact (overall). The metrics are exemplified, validated, and compared using a set of 10,778,696 MEDLINE articles matched to the Science Citation Index ExpandedTM. Articles are grouped into six 5-year periods (spanning 1983-2012) using publication year and into 6,159 fields constructed using comparable MeSH terms, with which each article is tagged. The analysis is conducted at the level of a field-period pair, of which 15,051 have articles and are used in this study. A factor analysis shows that transformativeness and impact are positively related (ρ = .402), but represent distinct phenomena. Looking at the subcomponents of transformativeness, there is no evidence that transformative work is adopted slowly or that the generation of important new concepts coincides with the obsolescence of existing concepts. We also find that the generation of important new concepts and highly cited work is more risky. Finally, supporting the validity of our metrics, we show that work that draws on a wider range of research fields is used more widely.

Background Many models and frameworks are currently used to classify or describe knowledge translation interventions to promote and integrate evidence into practice in healthcare. Methods We performed a scoping review of intervention classifications in public health, clinical medicine, nursing, policy, behaviour science, improvement science and psychology research published to May 2013 by searching MEDLINE, PsycINFO, CINAHL and the grey literature. We used five stages to map the literature: identifying the research question; identifying relevant literature; study selection; charting the data; collating, summarizing, and reporting results. Results We identified 51 diverse classification schemes, including 23 taxonomies, 15 frameworks, 8 intervention lists, 3 models and 2 other formats. Most documents were public health based, 55% included a literature or document review, and 33% were theory based. Conclusions This scoping review provides an overview of schemes used to classify interventions which can be used for evaluation, comparison and validation of existing and emerging models. The collated taxonomies can guide authors in describing interventions; adequate descriptions of interventions will advance the science of knowledge translation in healthcare.

The World Health Organization (WHO) Classification of Tumours: A Living Evidence Gap Map by Tumour Type (WCT EVI MAP) project aims to develop Evidence Gap Maps of the available evidence, primarily to inform the WHO Classification of Tumours. The project, covering all tumour types, faces the challenge of reviewing a huge number of studies by reviewers from multiple backgrounds. The aim was to develop a decision tree (DT) diagram for classifying study designs reporting on tumour pathology studies, in order to support the decision‐making process when assigning evidence levels across various disciplines. A modified consensus process, incorporating stakeholder workshops, was conducted in three phases: (1) development of the initial DT diagram draft (literature review and expert evaluation); (2) iterative reviews with project partners; and (3) testing the advanced DT diagram version with several sets of references to refine critical points. A total of 368 records were used for training throughout the entire process. Consensus was achieved when classifications could categorise studies consistently without causing discordance in new example sets. A DT diagram and its Glossary of Operational Definitions with 27 decision nodes and 26 categories were developed. The DT diagram is organised into six sections: WCT EVI MAP selection criteria, evidence synthesis, basic research related studies, descriptive studies, observational and experimental studies, and diagnostic test studies. The DT diagram is a valuable tool for the project's needs, successfully integrating diverse disciplinary perspectives for classifying evidence in tumour pathology research according to study design. It lays the foundation for future advancements in evidence mapping and classification within tumour pathology and related disciplines.

In Brazil, the CNPq (National Council for Scientific and Technological Development) provides grants, funds and fellowships to productive scientists to support their investigations. They are ranked and categorized into four hierarchical levels ranging from PQ 1A (the highest) to PQ 1D (the lowest). Few studies, however, report and analyse scientific productivity in different sub-fields of Biomedical Sciences (BS), e.g., Biochemistry, Pharmacology, Biophysics and Physiology. In fact, systematic comparisons of productivity among the PQ 1 categories within the above sub-fields are lacking in the literature. Here, the scientific productivity of 323 investigators receiving PQ 1 fellowships (A to D levels) in these sub-fields of BS was investigated. The Scopus database was used to compile the total number of articles, citations, h-index values and authorship positions (first-, co- or last-listed author) in the most cited papers by researchers granted CNPq fellowships. We found that researchers from Pharmacology had the best performance for all of the parameters analysed, followed by those in Biochemistry. There was great variability in scientific productivity within the PQ 1A level in all of the sub-fields of BS, but not within the other levels (1B, 1C and 1D). Analysis of the most cited papers of PQ 1(A–D) researchers in Pharmacology revealed that the citations of researchers in the 1C and 1D levels were associated with publications with their senior supervisors, whereas those in the 1B level were less connected with their supervisors in comparison to those in 1A. Taken together, these findings suggest that the scientific performance of PQ 1A researchers in BS is not homogenous. In our opinion, parameters such as the most cited papers without the involvement of Ph.D. and/or post-doctoral supervisors should be used to make decisions regarding any given researcher’s fellowship award level.