Background: International Spinal Cord Injury (SCI) Data Sets have been developed and disseminated worldwide since 2006 to establish minimum standards for clinical data collection. Basic data sets (BDSs) are intended to ensure a uniform minimum level of information across clinical settings and research projects. In 2013, an International SCI Urinary Tract Infection (UTI) Basic Data Set was published. This BDS defined UTI using the 1992 US National Institute on Disability and Rehabilitation Research criteria: “new onset of symptoms plus bacteriuria, leukocyturia, and positive urine culture.” Since its publication, multiple new clinical and research definitions of UTI have emerged, creating the need for revision. Objectives: To update the International SCI UTI Basic Data Set to reflect contemporary consensus definitions and improve global consistency in clinical data collection. Methods: A qualitative, 3-phase revision process was conducted. Phase 1 involved a phenomenographic surface analysis of the content of the International SCI UTI, Lower Urinary Tract (LUT) Function, Urodynamics, and Core Data Sets, alongside items from the validated, patient-centered Urinary Symptom Questionnaires for Neurogenic Bladder (USQNB), which are differentiated by bladder management method (voiding, intermittent catheterization, and indwelling catheter). Phase 2 consisted of a member check-in during which preliminary findings from the surface analysis were reviewed and refined with a subject matter expert. Phase 3 involved structured discussion and consensus refinement by an international panel of subject matter experts. Results: The revision process produced an updated set of criteria for determining the likelihood of UTI in individuals with SCI, aligned with a recent (2024) international consensus study. A revised UTI Basic Data Set (version 2) was developed that includes 22 new variables intended to enhance the consistency and comparability of global clinical data collection; only 10 to 12 additional variables are required per case depending on bladder management method. Conclusion: The revised International SCI UTI Basic Data Set reflects contemporary consensus definitions and expands the scope of standardized variables, supporting more reliable and internationally harmonized clinical and research data collection for individuals with SCI.

Abstract Background The 2024 international reference standard for urinary tract infection (UTI) research scores four domains - symptoms and signs, systemic criteria, pyuria, and culture - to classify samples as No UTI, Possible UTI, Probable UTI, or Definite UTI. It identifies spinal cord injury (SCI) as a condition of impaired symptom perception, and states that catheter-associated UTI requires a separate standard. We applied it to confirmed-asymptomatic samples from adults with neurogenic lower urinary tract dysfunction (NLUTD) due to spinal cord injury or disease (SCI/D) who use intermittent catheterization (IC). Methods The reference standard was applied to 222 samples from 96 adults with NLUTD due to SCI/D using IC, for all of which the participant confirmed, using the Urinary Symptom Questionnaire for Neurogenic Bladder–Intermittent Catheter (USQNB-IC), that they had been asymptomatic at sampling and for 72 hours prior. Because no participant was febrile and no blood markers are drawn in this population, the systemic-criteria domain scored zero for every sample; the reported classifications are therefore a floor. Pyuria was scored under conservative and inclusive interpretations of categorical urinary white blood cell (WBC) bins. Results Under the conservative interpretation, 38.7% of samples were classified No UTI, 22.5% Possible UTI, and 38.7% Probable UTI; under the inclusive interpretation, 8.1% No UTI, 40.5% Possible UTI, and 51.4% Probable UTI. No sample reached Definite UTI, a structural consequence of the empty systemic domain. Of the samples that met the pyuria entry threshold, 136 of 136 (conservative) and 204 of 206 (inclusive) were classified Possible or Probable UTI: meeting the entry threshold determined the classification. Conclusions The consensus reference standard classifies 61–92% of confirmed-asymptomatic NLUTD-IC samples as Possible or Probable UTI, and 39–51% as Probable UTI - a floor estimate that could only rise if blood markers were available. This confirms the framework’s own prediction that a separate standard is needed for populations with altered symptom expression and baseline-positive urinary markers. Research in context Evidence before this study Urinary tract infection (UTI) is the most common secondary condition and infectious cause of hospitalisation among people with neurogenic lower urinary tract dysfunction due to spinal cord injury or disease, and it is over-diagnosed and over-treated in this population. Diagnosis has long rested on laboratory thresholds - pyuria and bacteriuria - despite repeated demonstrations that both are common in people who do not have or develop symptoms, and that neither is associated with symptom status in those who catheterize. In 2024 an international multidisciplinary Delphi consensus published a reference standard for UTI research that scores four domains, states that it systematically addressed all issues of diagnosis and nomenclature for research purposes, and extends explicitly to populations unable to perceive or express lower urinary tract symptoms, naming spinal cord injury as one such population. That standard has not, to our knowledge, been applied to people in this population whose symptom status was known, and no published study has examined whether the reference standard can be applied based on the results in routine clinical laboratory reports. Added value of this study We applied the reference standard to 222 urine samples from 96 community-dwelling adults with neurogenic lower urinary tract dysfunction who use intermittent catheterisation, each of whom confirmed, using an instrument validated in this population, that they had been asymptomatic for the 72 hours before the sample was taken. The standard assigns one point toward a UTI classification to individuals with spinal cord injury without requiring them to endorse any specific symptom, and in this group of asymptomatic individuals, between 61% and 92% of their samples were classified as Possible or Probable UTI. The range of misclassification depends on how the pyuria criteria are applied: a large clinical laboratory provides leukocyte counts in bins that overlap the reference standard criteria at the low (0-5 per hpf) and high (40-60 per hpf) ends, whereas a research laboratory might instead provide exactly the values requested. Implications of all the available evidence A reference standard intended to define UTI for research is intended for selecting participants, adjudicating patient and research outcomes, and auditing practice. If it classifies the majority of samples from people who report no symptoms as Possible or Probable UTI, it will inflate apparent infection rates, systematically misclassify controls, and give the appearance of evidence to treatment decisions that the underlying data do not support. This is demonstrated within a population already subject to substantial antimicrobial over-use, those with neurogenic lower urinary tract dysfunction due to spinal cord injury or disease. Two remedies follow directly. The capability question should be answered from the patient, using instruments that are validated for the patient’s primary method of bladder management, rather than from the diagnosis; and any standard intended for routine as well as research settings should be scoreable from the results laboratories actually issue, with explicit instructions for categorical bins that straddle its thresholds and for results that are categorical rather than quantitative.

Abstract Urinary tract infection (UTI) remains the most common infectious complication among individuals with neurogenic lower urinary tract dysfunction (NLUTD) due to spinal cord injury or disease (SCI/D). Despite widespread reliance on microbiological and symptom-based criteria for UTI diagnosis, significant ambiguity persists—especially in distinguishing clinically meaningful change from normal variability in urinary analysis results. This uncertainty contributes to overdiagnosis, inappropriate antibiotic use, and antimicrobial resistance. The present study seeks to operationalize “normal variability” of the urinary microbiome (urobiome) among adults with SCI/D. Using repeated samples collected from asymptomatic individuals over time, we analyzed inter- and intra-individual microbial composition to determine stability and fluctuation under baseline conditions. We observed wide intra- and inter-individual variability, substantial overlap between asymptomatic and pre-symptomatic states, and a consistent predominance of genera conventionally labeled as “uropathogens” even in the absence of symptoms. These findings suggest that assumptions drawn from cross-sectional studies—linking particular taxa or diversity values to health or disease—are not supported within individuals over time, at least in people with NLUTD. This study provides a foundation for distinguishing expected variation from those potentially related to infection, supporting development of precision-based diagnostic thresholds. Results offer critical insight into the ecological dynamics of the urobiome among people with NLUTD who are asymptomatic, establishes a methodological precedent for urobiome-informed clinical decision-making in SCI/D populations, and provides a foundation for distinguishing expected variation from those potentially related to infection, supporting development of precision-based diagnostic thresholds. By identifying personalized baselines and patterns of change, we aim to support research designed to obtain actionable information from the urobiome to enhance the accuracy and stewardship of UTI diagnosis and treatment in this high-risk population.

Abstract Background Urinary tract infection (UTI) is the most common secondary condition among people with spinal cord injury/disease (SCI/D). Intravesical Lacticaseibacillus rhamnosus GG (LGG) is an antibiotic-sparing approach to managing urinary symptoms. Objective Determine the optimal number of doses of intravesical LGG for urinary symptom reduction. Design Prospective, randomized, two-arm dosing trial. Setting National recruitment with a local subsample providing urine samples in Washington, DC, USA. Participants Adults with SCI/D and neurogenic lower urinary tract dysfunction (NLUTD) who use intermittent catheterization (IC); 177 enrolled and randomized (intention-to-treat), with 76 compliant instillers (39 low-dose, 37 high-dose) in the per-protocol analytic sample. Interventions Two (2 doses/24 hours) or four (4 doses/36 hours) intravesical LGG regimens, self-initiated in response to cloudier or malodorous urine per the Self-Management Protocol using Probiotics (SMP-Pro). Main Outcome Measures Primary: proportion achieving ≥20% reduction on the Urinary Symptom Questionnaire for Neurogenic Bladder–Intermittent Catheter version (USQNB-IC). Secondary: urinary biomarkers (leukocyte esterase, nitrite, white blood cells, urinary neutrophil gelatinase-associated lipocalin [uNGAL]) and standard urine culture (SUC) in a local subsample. Results By Day 2, 57.9% (63.8% low-dose; 51.2% high-dose) achieved ≥20% total symptom reduction; high-dose success rose to 70.0% by Day 4. Thirty percent of high-dose participants did not respond at either time point and could not be distinguished from responders by demographics or urine biomarkers. Urinary biomarkers and SUC were unchanged pre- to post-instillation. No serious adverse events were adjudicated as attributable to intravesical LGG by an independent Data Safety Monitoring Board (DSMB). Conclusions A two-dose course of intravesical LGG yields clinically meaningful symptom improvement in the majority of people with SCI/D and NLUTD who use IC; four doses benefits a meaningful subgroup of two-day non-responders, while a small cohort remains nonresponsive. These results provide preliminary dosing guidance and support progression to a definitive trial.

Ethical statistical practice requires a professional commitment to the "fitness for purpose" of both data and statistical products. While the UN Fundamental Principles of Official Statistics and the European Statistical System Code of Practice identify "relevance" as a core objective, the American Statistical Association (ASA) Ethical Guidelines frame "fitness for purpose" as an explicit ethical obligation. This paper argues that fitness for purpose constitutes a minimum ethical standard for all statistical practice, with critical implications for Indigenous data and other communities with specific needs, such as rare disease populations. We demonstrate that co-designing projects with Indigenous communities ensures data are fit for purpose and support sovereign decision-making. By embedding fitness for purpose throughout the statistical lifecycle, practitioners improve decision-making and avoid harmful deficit-based framings. Our analysis shows how operational tools such as the CARE Principles for Indigenous Data Governance and the UN Human Rights-Based Approach to Data, are realized through ethical practice and purposeful co-design. Finally, we present a tool for data projects to explicate intended purposes, enabling the fitness of data collection and statistical products to be systematically determined and documented across diverse stakeholder contexts.

Background:Complicated urinary tract infection (cUTI) is prevalent among people with spinal cord injury and disease (SCI/D). Diagnostic guidelines are neither consistent nor evidence based. Objectives:To establish consensus around symptoms-based diagnostic and decision-making criteria for cUTI for SCI/D. Methods:A representative sample of clinicians from PM&R, infectious disease, urology, and primary care within the United States (phase 1) and internationally (phase 2) participated in this study. Phase 1 involved focus groups and interviews to refine a decision-making paradigm for cUTI based on reliable and validated Urinary Symptom Questionnaires for Neurogenic Bladder (USQNBs: intermittent catheterization, indwelling catheterization, and voider versions). The phase 2 international Delphi survey on cUTI diagnostic criteria reflected phase 1 results. These criteria feature 6 "profiles": combinations of symptom number and types with associated likelihood of cUTI for each USQNB (18 total decisions). Results:Analyses of the phase 1 transcripts (n = 32) led to the Delphi design. Across the United States and internationally, 24 responses were obtained on the complete Delphi, with 48 responses on the USQNB for intermittent catheterization only. We achieved the a priori target 80% consensus on 13 of 18 decisions. The remaining 5 decisions reached 62.2% to 77.8% agreement. Changes were made based on respondent suggestions to clarify decisions and slightly modify risk descriptors. One hundred percent consensus among subject matter experts from 9 collaborating SCI model systems centers was achieved for the revisions. Conclusion:This is the first international and empirical initiative to establish cUTI symptoms-based guidelines for cUTI in SCI/D. These guidelines provide a coherent and evidence-based approach to decision making based on symptoms for clinicians and patients.

[This corrects the article DOI: 10.1371/journal.pone.0293879.].

Tractenberg, Piercey, and Buell 2024 presented a list of 44 proto-Guidelines for Ethical Mathematical Practice, developed through examination of codes of ethics of adjacent disciplines and consultation with members of the mathematics community, and gave justifications for the use of these proto-Guidelines. We propose formatting the list as a deck of 44 cards and describe ways to use the cards in classes at any stage of the undergraduate mathematics program. A simple game or encounter with the cards can be used exclusively as an introduction, or the cards can be used repeatedly in order to help students move to higher levels of achievement with respect to the proto-Guidelines and ethical reasoning in general. We present, in Appendix A, a sample semester long sequence of assignments for such a purpose, with activities at various levels of Blooms taxonomy.

The Mastery Rubric for Scientific Thinking (MR-ST) specifies a learnable, improvable set of knowledge, skills, and abilities (KSAs) and performance stages that support rigorous, reproducible research across disciplines and data modalities. A Mastery Rubric (MR) is a curriculum development and evaluation tool that makes expected growth explicit by aligning KSAs to observable performance-level descriptors. MRs are developed to guide instruction, assessment, and program evaluation. Building on stewardship as a cross-disciplinary ideal and curriculum models developed for bioinformatics and for statistics/data science, the MR-ST defines eleven KSAs—ethical practice; prerequisite disciplinary knowledge; interdisciplinarity; problem definition via critical review; hypothesis generation; experimental/study design; identifying/collecting relevant data; selecting/using appropriate analytical methods; interpretation; drawing/contextualizing conclusions; and communication. Using cognitive task analysis and a guild-based developmental trajectory (Novice → Beginner → Apprentice → early Journeyman → late Journeyman), the Mastery Rubric for Scientific Thinking describes concrete, observable performance level descriptors featuring Bloom’s taxonomy and psychometric validity criteria. The MR-ST is designed to accommodate qualitative, quantitative, and mixed methods, and to promote rigor and integrity in every domain. The MR-ST is strongly aligned with a systematicity view of scientific knowledge, supporting curricular outcomes about research that fit a flexible, modern characterization of science and contributions made to scientific knowledge. The result is a transparent, evidence-based tool for curriculum and instructional design, including targeted and actionable assessment, and program evaluation that makes independence and stewardship in research accessible and assessable from early undergraduate study through advanced professional preparation.

Instructors in higher education are increasingly expected to integrate emerging content—such as ethical reasoning, artificial intelligence, or data literacy—into existing courses, often without adequate support, guidance, or alignment with evidence-based practices. This paper introduces a structured and research-informed paradigm for instructor-led course redesign: Integration not Concatenation (InC). InC equips instructors to revise their own syllabi, learning outcomes, and assessments in ways that embed new material coherently and evaluably, without disrupting the cognitive architecture of their courses. The InC paradigm is grounded in two cognitive-science-informed models of change: the augmented Diffusion of Innovations (aDOI) and the augmented Theory of Change Management (aToCM). These models reframe curricular change as a cognitively demanding process and offer practical guidance for supporting instructors through message framing, knowledge transfer, alignment, and feedback. The paper details how InC operationalizes these models in a multi-phase intervention—the Hackathon of Educational Materials—that builds instructional agility, coherence, and evaluability. We synthesize research from curriculum theory, backward design, cognitive load theory, and cognitive complexity to support InC’s theoretical and practical claims. Outcomes include alignment of course elements, enhanced instructor confidence, and support for higher-order student learning. The paper concludes with implications for broader adoption, including use in faculty development, general education reform, and evidence-based instructional design. InC offers a fidelity-focused, scalable, and actionable strategy to support instructor-led change—bridging the persistent gap between educational research and pedagogical practice.

Statistical literacy is critically underappreciated across scientific disciplines, and is not part of the training in graduate programs even when “statistics” or “statistical methods” are. It is common to teach “introductory statistics” courses to promote engagement with key concepts and methods in the domain, but statistical literacy involves the selection of appropriate methods from among competing plausible alternatives, as well as the correct application of the methods, and then reasoning with those methods and their results. A new, developmental, model of statistical literacy that is specific for the scientific practitioner was outlined by Tractenberg (2017-a), based on prior models of statistical and scientific thinking. In this model, there are nine elements to statistical literacy: 1) Define a problem based on critical literature review; 2) Identify or choose – and justify - the measurement system; 3) Design the collection of data; 4) Pilot, analysis and interpretation; 5) Discern “exploratory”, “planned”, and “unplanned” data analysis; 6) Hypothesis generation based on planned & unplanned analyses; 7) Interpretation of results; 8) Drawing and contextualize conclusions; and 9) Communication. These knowledge, skills, and abilities (KSAs) are important for an individual to execute when they are doing scientific work, but these KSAs must also be developed to a sufficient level to permit an individual to assess and consume research articles and arguments that use data and inferences. The nine KSAs that comprise statistical literacy for the practicing scientist each require considerably more cognitive capacity than “engagement with key concepts and methods”. The aim of this paper is to discuss the relationship between purposeful development of these KSAs and Bloom’s taxonomy of educational outcomes (cognitive) so that statistical literacy can be clearly seen as a learnable, improvable skill set that can be introduced in the context of a journal club, among other options, and can continue to be used and developed by individuals and instructors.

A four-phase Hackathon of Educational Materials was developed to help instructors integrate, rather than concatenate, new content into existing courses. The “integrate, not concatenate” (InC) approach was piloted with ethical reasoning content in mathematics, statistics, operations research, and data science programs. The InC Hackathon phases were: Hack 1 (H1): Select feasible published ethical reasoning learning outcomes; Hack 2 (H2): Revise course learning outcomes to include ethics and assess the syllabus’s emphasis on ethical practice; Hack 3 (H3): Identify where new outcomes align within the course; Hack 4 (H4): Design assignments to assess these outcomes.The Hackathon took place over eight hours across three days. Originally planned for 4-5 instructors and one course, it engaged 14 instructors, integrating ethical reasoning into seven courses across three programs. Sequenced learning objectives were developed to progressively build students' ethical reasoning. While Hacks 1-3 were completed, Hack 4 required more time, and participants recommended leadership orientation and curriculum-wide alignment for future iterations.This pilot demonstrated a reproducible framework for systemic and systematic curriculum changes in higher education, effectively embedding ethical reasoning content while fostering faculty collaboration and innovation.

The inclusion of human sex and gender data in statistical analysis invokes multiple considerations for data collection, combination, analysis, and interpretation. These considerations are not unique to variables representing sex and gender. However, considering the relevance of the ethical practice standards for statistics and data science to sex and gender variables is timely, with results that can be applied to other sociocultural variables. Historically, human gender and sex have been categorized with a binary system. This tradition persists mainly because it is easy, and not because it produces the best scientific information. Binary classification simplifies combinations of older and newer data sets. However, this classification system eliminates the ability for respondents to articulate their gender identity, conflates gender and sex, and also obscures potentially important differences by collapsing across valid and authentic categories. This approach perpetuates historical inaccuracy, simplicity, and bias, while also limiting the information that emerges from analyses of human data. The approach also violates multiple elements in the American Statistical Association (ASA) Ethical Guidelines for Statistical Practice. Information that would be captured with a nonbinary classification could be relevant to decisions about analysis methods and to decisions based on otherwise expert statistical work. Statistical practitioners are increasingly concerned with inconsistent, uninformative, and even unethical data collection and analysis practices. This paper presents a historical introduction to the collection and analysis of human gender and sex data, offers a critique of a few common survey questioning methods based on alignment with the ASA Ethical Guidelines, and considers the scope of ethical considerations for human gender and sex data from design through analysis and interpretation.

Case studies are typically used to teach 'ethics', but in quantitative courses it can seem distracting, for both instructor and learner, to introduce a case analysis. Moreover, case analyses are typically focused on issues relating to people: obtaining consent, dealing with research team members, and/or potential institutional policy violations. While relevant to some research, not all students in quantitative courses plan to become researchers, and ethical practice is an essential topic for students of of mathematics, statistics, data science, and computing regardless of whether or not the learner intends to do research. Ethical reasoning is a way of thinking that requires the individual to assess what they know about a potential ethical problem (their prerequisite knowledge), and in some cases, how behaviors they observe, are directed to perform, or have performed, diverge from what they know to be ethical behavior. Ethical reasoning is a learnable, improvable set of knowledge, skills, and abilities that enable learners to recognize what they do and do not know about what constitutes 'ethical practice' of a discipline, and in some cases, to contemplate alternative decisions about how to first recognize, and then proceed past, or respond to, such divergences. A stakeholder analysis is part of prerequisite knowledge, and can be used whether there is or is not an actual case or situation to react to. In courses with mainly quantitative content, a stakeholder analysis is a useful tool for instruction and assessment. It can be used to both integrate authentic ethical content and encourage careful quantitative thought. It is a mistake to treat 'training in ethical practice' and 'training in responsible conduct of research' as the same thing. This paper discusses how to introduce ethical reasoning, stakeholder analysis, and ethical practice standards authentically in quantitative courses.

Artificial Intelligence (AI) is a field that utilizes computing and often, data and statistics, intensively together to solve problems or make predictions. AI has been evolving with literally unbelievable speed over the past few years, and this has led to an increase in social, cultural, industrial, scientific, and governmental concerns about the ethical development and use of AI systems worldwide. The ASA has issued a statement on ethical statistical practice and AI (ASA, 2024), which echoes similar statements from other groups. Here we discuss the support for ethical statistical practice and ethical AI that has been established in long-standing human rights law and ethical practice standards for computing and statistics. There are multiple sources of support for ethical statistical practice and ethical AI deriving from these source documents, which are critical for strengthening the operationalization of the "Statement on Ethical AI for Statistics Practitioners". These resources are explicated for interested readers to utilize to guide their development and use of AI in, and through, their statistical practice.

Practitioners in quantitative fields can encounter challenges when they need to communicate about their work. Transparency, the hallmark of ethical quantitative practice, requires specificity in levels of certainty, assumptions, sensitivity analyses and replicability. These considerations add to the increasingly complex computational, mathematical, and statistical modeling methods that must also be coherently reported for stakeholders. This paper describes the augmented Diffusion of Innovation (aDOI) Change Theory as a way for quantitative practitioners to comprehend and align the complexity of their message with the cognitive complexity capabilities of the hearer of the message. Kotter's Theory of Change Management (ToCM), a longstanding model for workplace change initiative leadership, is reenvisioned with the cognitive complexities of the change made explicit. Together, aDOI describes how ToCM can be facilitated by 1) creating evidence-based structure for how communication of new information or a change initiative can be optimized when cognitive complexity of the message and the hearer are leveraged; and 2) recognizing new opportunities for engagement and buy-in that emerge when the specific elements of aDOI are mapped onto the ToCM activities and phases. The combination of aDOI and ToCM outlines the importance of a nuanced understanding of how individuals and organizations internalize and apply change. Combining the theoretical underpinnings of how innovations spread with a practical, stepwise approach to implementing change offers a powerful tool for leaders to create change, and to manage transitions effectively. It also offers opportunities for individuals to create their own "change initiatives", and to document the accomplishment of subgoals along the way. The combination of aDOI and ToCM can help individuals and leaders not only advocate for change but also navigate the intricate process of engaging others as they co-navigate the stages of acceptance and execution.

Preprint of manuscript in press (2024), Journal of Science & Engineering Ethics. Peer reviewed manuscript based on 2022 White Paper, https://doi.org/10.48550/arXiv.2209.09311. This project explored what constitutes “ethical practice of mathematics”. Thematic analysis of ethical practice standards from mathematics-adjacent disciplines (statistics and computing), were combined with two organizational codes of conduct and community input resulting in over 100 items. These analyses identified 29 of the 52 items in the 2018 American Statistical Association Ethical Guidelines for Statistical Practice, and 15 of the 24 additional (unique) items from the 2018 Association of Computing Machinery Code of Ethics for inclusion. Three of the 29 items synthesized from the 2019 American Mathematical Society Code of Ethics, and zero of the Mathematical Association of America Code of Ethics, were identified as reflective of “ethical mathematical practice” beyond items already identified from the other two codes. The community contributed six unique items. Item stems were standardized to, “The ethical mathematics practitioner…”. Invitations to complete the 30-minute online survey were shared nationally (US) via Mathematics organization listservs and other widespread emails and announcements. We received 142 individual responses to the national survey, 75% of whom endorsed 41/52 items, with 90-100% endorsing 20/52 items on the survey. Items from different sources were endorsed at both high and low rates. These results suggest that the community perceives a much wider range of behaviors by mathematicians to be subject to ethical practice standards than had been previously included in professional organization codes. The results provide evidence against the argument that mathematics practitioners engaged in "pure" or "theoretical" work have minimal, small, or no ethical obligations.

This project explored what constitutes "ethical practice of mathematics". Thematic analysis of ethical practice standards from mathematics-adjacent disciplines (statistics and computing), were combined with two organizational codes of conduct and community input resulting in over 100 items. These analyses identified 29 of the 52 items in the 2018 American Statistical Association Ethical Guidelines for Statistical Practice, and 15 of the 24 additional (unique) items from the 2018 Association of Computing Machinery Code of Ethics for inclusion. Three of the 29 items synthesized from the 2019 American Mathematical Society Code of Ethics, and zero of the Mathematical Association of America Code of Ethics, were identified as reflective of "ethical mathematical practice" beyond items already identified from the other two codes. The community contributed six unique items. Item stems were standardized to, "The ethical mathematics practitioner…". Invitations to complete the 30-min online survey were shared nationally (US) via Mathematics organization listservs and other widespread emails and announcements. We received 142 individual responses to the national survey, 75% of whom endorsed 41/52 items, with 90-100% endorsing 20/52 items on the survey. Items from different sources were endorsed at both high and low rates. A final thematic analysis yielded 44 items, grouped into "General" (12 items), "Profession" (10 items) and "Scholarship" (11 items). Moreover, for the practitioner in a leader/mentor/supervisor/instructor role, there are an additional 11 items (4 General/7 Professional). These results suggest that the community perceives a much wider range of behaviors by mathematicians to be subject to ethical practice standards than had been previously included in professional organization codes. The results provide evidence against the argument that mathematics practitioners engaged in "pure" or "theoretical" work have minimal, small, or no ethical obligations.

Objectives To determine whether assessment and decision-making around urinary symptoms in people with neurogenic lower urinary tract dysfunction (NLUTD) should depend on bladder management. Methods Three surveys of urinary symptoms associated with NLUTD (USQNBs) were designed specific to bladder management method for those who manage their bladders with indwelling catheter (IDC), intermittent catheter (IC), or voiding (V). Each was deployed one time to a national sample. Subject matter experts qualitatively assessed the wording of validated items to identify potential duplicates. Clustering by unsupervised structural learning was used to analyze duplicates. Each item was classified into mutually exclusive and exhaustive categories: clinically actionable (“fever”), bladder-specific (“suprapubic pain”), urine quality (“cloudy urine”), or constitutional (“leg pain”). Results A core of 10 “NLUTD urinary symptoms” contains three clinically actionable, bladder-specific, and urine quality items plus one constitutional item. There are 9 (IDC), 11 (IC), and 8 (V) items unique to these instruments. One decision-making protocol applies to all instruments. Conclusion Ten urinary symptoms in NLUTD are independent of bladder management, whereas a similar number depend on bladder management. We conclude that assessment of urinary symptoms for persons with NLUTD should be specific to bladder management method, like the USQNBs are.

Artificial Intelligence (AI) arises from computing and statistics, and as such, can be developed and deployed ethically when the ethical practice standards of each of these fields are followed. The Toronto Declaration was formulated in 2018 specifically to ensure that machine learning and AI could be held accountable for respecting, and promoting, universal human rights. The Code of Ethics and Professional Conduct of the Association of Computing Machinery (ACM, 2018) and the Ethical Guidelines for Statistical Practice of the American Statistical Association (ASA, 2022) describe the ethical practice standards for any person at any level of training or job title who utilizes computing (ACM) or statistical practices (ASA). These three reference documents can together define "what is ethical AI". All development, deployment, and use of computing is covered by the ACM Code; the ASA defines statistical practice to "include activities such as: designing the collection of, summarizing, processing, analyzing, interpreting, or presenting, data; as well as model or algorithm development and deployment.” Just as the Toronto Declaration describes universal human rights protections, the ACM and ASA ethical practice standards apply to professionals, individuals with diverse background or jobs that include computing and statistical practices at any point, and employers, clients, organizations, and institutions that employ or utilize the outputs from computing and statistical practices worldwide. The ACM Code of Ethics has four Principles, including one specifically for Leaders with seven elements. The ASA Ethical Guidelines include eight principles and an Appendix; one Guideline Principle (G. Responsibilities of Leaders, Supervisors, and Mentors in Statistical Practice) with its five elements and the Appendix (Responsibilities of organizations/institutions) with its 12 elements are specifically intended to support workplace engagement with, and support of, ethical statistical practices, plus, the specific roles and responsibilities of those in leadership positions. These ethical practice standards can support both individual practitioners', and leaders', meeting their obligations for ethical AI worldwide.