BACKGROUND: Concern has been raised about the quality of available biological variation (BV) estimates and the effect of their application in clinical practice. A European Federation of Clinical Chemistry and Laboratory Medicine Task and Finish Group has addressed this issue. The aim of this report is to (a) describe the Biological Variation Data Critical Appraisal Checklist (BIVAC), which verifies whether publications have included all essential elements that may impact the veracity of associated BV estimates, (b) use the BIVAC to critically appraise existing BV publications on enzymes, lipids, kidney, and diabetes-related measurands, and (c) apply metaanalysis to deliver a global within-subject BV (CV_I) estimate for alanine aminotransferase (ALT). METHODS: In the BIVAC, publications were rated as A, B, C, or D, indicating descending compliance for 14 BIVAC quality items, focusing on study design, methodology, and statistical handling. A D grade indicated that associated BV estimates should not be applied in clinical practice. Systematic searches were applied to identify BV studies for 28 different measurands. RESULTS: In total, 128 publications were identified, providing 935 different BV estimates. Nine percent achieved D scores. Outlier analysis and variance homogeneity testing were scored as C in >60% of 847 cases. Metaanalysis delivered a CV_I estimate for ALT of 15.4%. CONCLUSIONS: Application of BIVAC to BV publications identified deficiencies in required study detail and delivery, especially for statistical analysis. Those deficiencies impact the veracity of BV estimates. BV data from BIVAC-compliant studies can be combined to deliver robust global estimates for safe clinical application.

Background: In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notable ethical concerns. Content: This study delineates AI’s dual role in scientific publishing: as a co-creator in the writing and review of scientific papers and as an ethical challenge. We first explore the potential of AI as an enhancer of efficiency, efficacy, and quality in creating scientific papers. A critical assessment follows, evaluating the risks vs. rewards for researchers, especially those early in their careers, emphasizing the need to maintain a balance between AI’s capabilities and fostering independent reasoning and creativity. Subsequently, we delve into the ethical dilemmas of AI’s involvement, particularly concerning originality, plagiarism, and preserving the genuine essence of scientific discourse. The evolving dynamics further highlight an overlooked aspect: the inadequate recognition of human reviewers in the academic community. With the increasing volume of scientific literature, tangible metrics and incentives for reviewers are proposed as essential to ensure a balanced academic environment. Summary: AI’s incorporation in scientific publishing is promising yet comes with significant ethical and operational challenges. The role of human reviewers is accentuated, ensuring authenticity in an AI-influenced environment. Outlook: As the scientific community treads the path of AI integration, a balanced symbiosis between AI’s efficiency and human discernment is pivotal. Emphasizing human expertise, while exploit artificial intelligence responsibly, will determine the trajectory of an ethically sound and efficient AI-augmented future in scientific publishing.

Data on biological variation are used for many purposes in laboratory medicine but concern exists over the validity of the data reported in some studies. A critical appraisal checklist has been produced by a working group established by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) to enable standardised assessment of existing and future publications of biological variation data. The checklist identifies key elements to be reported in studies to enable safe accurate and effective transport of biological variation data sets across healthcare systems. The checklist is mapped to the domains of a minimum data set required to enable this process.

BACKGROUND: The European Federation of Clinical Chemistry and Laboratory Medicine European Biological Variation Study (EuBIVAS) has been established to deliver rigorously determined data describing biological variation (BV) of clinically important measurands. Here, EuBIVAS-based BV estimates of serum electrolytes, lipids, urea, uric acid, total protein, total bilirubin, direct bilirubin, and glucose, as well as their associated analytical performance specifications (APSs), are presented. METHOD: Samples were drawn from 91 healthy individuals (38 male, 53 female; age range, 21–69 years) for 10 consecutive weeks at 6 European laboratories. Samples were stored at −80 °C before duplicate analysis of all samples on an ADVIA 2400 (Siemens Healthineers). Outlier and homogeneity analyses were performed, followed by CV-ANOVA on trend-corrected data, when relevant, to determine BV estimates with CIs. RESULTS: The within-subject BV (CV_I) estimates of all measurands, except for urea and LDL cholesterol, were lower than estimates available in an online BV database, with differences being most pronounced for HDL cholesterol, glucose, and direct bilirubin. Significant differences in CV_I for men and women/women <50 years of age were evident for uric acid, triglycerides, and urea. The CV_A obtained for sodium and magnesium exceeded the EuBIVAS-based APS for imprecision. CONCLUSIONS: The EuBIVAS, which is fully compliant with the recently published Biological Variation Data Critical Appraisal Checklist, has produced well-characterized, high-quality BV estimates utilizing a stringent experimental protocol. These new reference data deliver revised and more exacting APS and reference change values for commonly used clinically important measurands, thus having direct relevance to diagnostics manufacturers, service providers, clinical users, and ultimately patients.

Background: Biological variation (BV) data have many fundamental applications in laboratory medicine. At the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) the reliability and limitations of current BV data were discussed. The EFLM Working Group on Biological Variation is working to increase the quality of BV data by developing a European project to establish a biobank of samples from healthy subjects to be used to produce high quality BV data. Methods: The project involved six European laboratories (Milan, Italy; Bergen, Norway; Madrid, Spain; Padua, Italy; Istanbul, Turkey; Assen, The Netherlands). Blood samples were collected from 97 volunteers (44 men, aged 20–60 years; 43 women, aged 20–50 years; 10 women, aged 55–69 years). Initial subject inclusion required that participants completed an enrolment questionnaire to verify their health status. The volunteers provided blood specimens once per week for 10 weeks. A short questionnaire was completed and some laboratory tests were performed at each sampling consisting of blood collected under controlled conditions to provide serum, K₂EDTA-plasma and citrated-plasma samples. Results: Samples from six out of the 97 enroled subjects were discarded as a consequence of abnormal laboratory measurements. A biobank of 18,000 aliquots was established consisting of 120 aliquots of serum, 40 of EDTA-plasma, and 40 of citrated-plasma from each subject. The samples were stored at –80 °C. Conclusions: A biobank of well-characterised samples collected under controlled conditions has been established delivering a European resource to enable production of contemporary BV data.

Background: Alanine aminotransferase (ALT), aspartate aminotransferase (AST) and γ-glutamyl transferase (GGT) are enzymes measured in serum or plasma to investigate liver disease. The aim of this work is to assess the validity of published biological variation (BV) data currently available for these enzymes. Methods: Publications containing BV data for ALT, AST and GGT were identified by searching PubMed using the following keywords: biological varia*, RCV, CV_w, CV_i, CV_b, and CV_g. The 95% confidence intervals for the within- and between-subject coefficients of variation were calculated using the analytical imprecision, the number of subjects, samples and replicates. Results: The searches identified 10 publications with ALT, 14 with AST and nine with GGT data. The protocols presented in those publications as used were varied. The ranges of within-subject variation reported were: ALT: 11.1%–58.1%, AST: 3.0%–32.3% and for GGT: 3.9%–14.5%. The median values (ALT: 18.0%, AST: 11.9% and GGT: 13.8%) were similar to those listed in a BV database commonly used as a reference source. Conclusions: Published BV data for ALT, AST and GGT demonstrate a wide range of values derived from inconsistent protocols. The quality of the presentations of the data is variable. These findings raise concerns around the utility of the data currently available and highlight the need for critical appraisal of such publications. The working group on BV of the European Federation of Clinical Chemistry and Laboratory Medicine is undertaking work to develop a critical appraisal checklist for the production and publication of reliable BV data.