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An international team of biologists, philosophers, and historians of science explores the critically important process of replication in biological and biomedical research. Without replication, the trustworthiness of scientific research remains in doubt. Although replication is increasingly recognized as a central problem in many scientific disciplines, repeating the same scientific observations of experiments or reproducing the same set of analyses from existing data is remarkably difficult. In this important volume, an international team of biologists, philosophers, and historians of science addresses challenges and solutions for valid replication of research in medicine, ecology, natural history, agriculture, physiology, and computer science. After the introduction to important concepts and historical background, the book offers paired chapters that provide theoretical overviews followed by detailed case studies. These studies range widely in topics, from infectious-diseases and environmental monitoring to museum collections, meta-analysis, bioinformatics, and more. The closing chapters explicate and quantify problems in the case studies, and the volume concludes with important recommendations for best practices. -- Provided by publisher.

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes. The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions.

Background Well designed and properly executed randomised trials are considered the most reliable evidence on the benefits of healthcare interventions. However, there is overwhelming evidence that the quality of reporting is not optimal. The CONSORT (Consolidated Standards of Reporting Trials) statement was designed to improve the quality of reporting and provides a minimum set of items to be included in a report of a randomised trial. CONSORT was first published in 1996, then updated in 2001 and 2010. Here, we present the updated CONSORT 2025 statement, which aims to account for recent methodological advancements and feedback from end users. Methods We conducted a scoping review of the literature and developed a project-specific database of empirical and theoretical evidence related to CONSORT, to generate a list of potential changes to the checklist. The list was enriched with recommendations provided by the lead authors of existing CONSORT extensions (Harms, Outcomes, Non-pharmacological Treatment), other related reporting guidelines (TIDieR) and recommendations from other sources (e.g., personal communications). The list of potential changes to the checklist was assessed in a large, international, online, three-round Delphi survey involving 317 participants and discussed at a two-day online expert consensus meeting of 30 invited international experts. Results We have made substantive changes to the CONSORT checklist. We added seven new checklist items, revised three items, deleted one item, and integrated several items from key CONSORT extensions. We also restructured the CONSORT checklist, with a new section on open science. The CONSORT 2025 statement consists of a 30-item checklist of essential items that should be included when reporting the results of a randomised trial and a diagram for documenting the flow of participants through the trial. To facilitate implementation of CONSORT 2025, we have also developed an expanded version of the CONSORT 2025 checklist, with bullet points eliciting critical elements of each item. Conclusions Authors, editors, reviewers, and other potential users should use CONSORT 2025 when writing and evaluating manuscripts of randomised trials to ensure that trial reports are clear and transparent.

Reviewing and accepting study plans before results are known can counter perverse incentives. Chris Chambers sets out three ways to improve the approach. Reviewing and accepting study plans before results are known can counter perverse incentives. Chris Chambers sets out three ways to improve the approach.

•Due to the risks of identification and recruitment bias, opting for a cluster design when individual randomization would be feasible needs a strong justification. Concerns around contamination are unlikely to be acceptable justifications; although estimation of indirect effects might be.•When cluster randomization is adopted, we recommend that authors provide a clear justification for the choice of cluster randomization and clearly outline strategies to mitigate increased risks of bias. This should include identification and recruitment by someone blind to the treatment allocation and minimal or objective individual-level eligibility criteria.•Other good conduct procedures which are routinely implemented in individually randomized trials should be followed. These include implementation of the randomization using an accepted method of allocation concealment, for example, by using an independent statistician to generate the allocation sequence; blind outcome assessment when outcomes are subjective; and clear pre-specification (in a protocol or trial registration site) of the primary outcome including primary assessment time and method of primary analysis.•All these aspects should be clearly reported as per CONSORT guidelines. To ensure particular clarity around identification and recruitment, authors should also provide a timeline-cluster diagram. To describe the prevalence of risks of bias in cluster-randomized trials of individual-level interventions, according to the Cochrane Risk of Bias tool. Review undertaken in duplicate of a random sample of 40 primary reports of cluster-randomized trials of individual-level interventions. The most common reported reasons for adopting cluster randomization were the need to avoid contamination (17, 42.5%) and practical considerations (14, 35%). Of the 40 trials all but one was assessed as being at risk of bias. A majority (27, 67.5%) were assessed as at risk due to the timing of identification and recruitment of participants; many (21, 52.5%) due to an apparent lack of adequate allocation concealment; and many due to selectively reported results (22, 55%), arising from a mixture of reasons including lack of documentation of primary outcome. Other risks mostly occurred infrequently. Many cluster-randomized trials evaluating individual-level interventions appear to be at risk of bias, mostly due to identification and recruitment biases. We recommend that investigators carefully consider the need for cluster randomization; follow recommended procedures to mitigate risks of identification and recruitment bias; and adhere to good reporting practices including clear documentation of primary outcome and allocation concealment methods.

\"This book critically examines the historical and philosophical foundations of construct validity theory (CVT), and how these have and continue to inform and constrain the conceptualization of validity and its application in research. CVT has had an immense impact on how researchers in the behavioural sciences conceptualize and approach their subject matter. Yet, there is equivocation regarding the foundations of the CVT framework as well as ambiguities concerning the nature of the 'constructs' that are its raison d'etre. The book is organized in terms of three major parts that speak, respectively, to the historical, philosophical, and pragmatic dimensions of CVT. The primary objective is to provide researchers and students with a critical lens through which a deeper understanding may be gained of both the utility and limitations of CVT and the validation practices to which it has given rise.\"-- Back cover.

We examined the application and review materials of three calls (n= 2,823) of a prestigious grant for personal research funding in a national full population of early career scientists awarded by the Netherlands Organization for Scientific Research (NWO). Results showed evidence of gender bias in application evaluations and success rates, as well as in language use in instructions and evaluation sheets. Male applicants received significantly more competitive “quality of researcher” evaluations (but not “quality of proposal” evaluations) and had significantly higher application success rates than female applicants. Gender disparities were most prevalent in scientific disciplines with the highest number of applications and with equal gender distribution among the applicants (i.e., life sciences and social sciences). Moreover, content analyses of the instructional and evaluation materials revealed the use of gendered language favoring male applicants. Overall, our data reveal a 4% “loss” of women during the grant review procedure, and illustrate the perpetuation of the funding gap, which contributes to the underrepresentation of women in academia.