Generating and using real-world data: A worthwhile uphill battle.

The precision oncology paradigm challenges the feasibility and data generalizability of traditional clinical trials. Consequently, an unmet need exists for practical approaches to test many subgroups, evaluate real-world drug value, and gather comprehensive, accessible datasets to validate novel biomarkers. Real-world data (RWD) are increasingly recognized to have the potential to fill this gap in research methodology. Established applications of RWD include informing disease epidemiology, pharmacovigilance, and healthcare quality assessment. Currently, concerns regarding RWD quality and comprehensiveness, privacy, and biases hamper their broader application. Nonetheless, RWD may play a pivotal role in supplementing clinical trials, enabling conditional reimbursement and accelerated drug access, and innovating trial conduct. Moreover, purpose-built RWD repositories may support the extension or refinement of drug indications and facilitate the discovery and validation of new biomarkers. This perspective explores the potential of leveraging RWD to advance oncology, highlights its benefits and challenges, and suggests a path forward in this evolving field.

The Master Observational Trial: A New Class of Master Protocol to Advance Precision Medicine.

This commentary introduces a new clinical trial construct, the Master Observational Trial (MOT), which hybridizes the power of molecularly based master interventional protocols with the breadth of real-world data. The MOT provides a clinical venue to allow molecular medicine to rapidly advance, answers questions that traditional interventional trials generally do not address, and seamlessly integrates with interventional trials in both diagnostic and therapeutic arenas. The result is a more comprehensive data collection ecosystem in precision medicine.

Glia Accumulate Evidence that Actions Are Futile and Suppress Unsuccessful Behavior.

When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states. VIDEO ABSTRACT.

Neurophysiology of Human Perceptual Decision-Making.

The discovery of neural signals that reflect the dynamics of perceptual decision formation has had a considerable impact. Not only do such signals enable detailed investigations of the neural implementation of the decision-making process but they also can expose key elements of the brain's decision algorithms. For a long time, such signals were only accessible through direct animal brain recordings, and progress in human neuroscience was hampered by the limitations of noninvasive recording techniques. However, recent methodological advances are increasingly enabling the study of human brain signals that finely trace the dynamics of the unfolding decision process. In this review, we highlight how human neurophysiological data are now being leveraged to furnish new insights into the multiple processing levels involved in forming decisions, to inform the construction and evaluation of mathematical models that can explain intra- and interindividual differences, and to examine how key ancillary processes interact with core decision circuits.

Biochemical evidence accumulates across neurons to drive a network-level eruption.

Neural network computations are usually assumed to emerge from patterns of fast electrical activity. Challenging this view, we show that a male fly's decision to persist in mating hinges on a biochemical computation that enables processing over minutes to hours. Each neuron in a recurrent network contains slightly different internal molecular estimates of mating progress. Protein kinase A (PKA) activity contrasts this internal measurement with input from the other neurons to represent accumulated evidence that the goal of the network has been achieved. When consensus is reached, PKA pushes the network toward a large-scale and synchronized burst of calcium influx that we call an eruption. Eruptions transform continuous deliberation within the network into an all-or-nothing output, after which the male will no longer sacrifice his life to continue mating. Here, biochemical activity, invisible to most large-scale recording techniques, is the key computational currency directing behavior and motivational state.

Mailed fecal immunochemical test outreach for colorectal cancer screening: Summary of a Centers for Disease Control and Prevention-sponsored Summit.

Uptake of colorectal cancer screening remains suboptimal. Mailed fecal immunochemical testing (FIT) offers promise for increasing screening rates, but optimal strategies for implementation have not been well synthesized. In June 2019, the Centers for Disease Control and Prevention convened a meeting of subject matter experts and stakeholders to answer key questions regarding mailed FIT implementation in the United States. Points of agreement included: 1) primers, such as texts, telephone calls, and printed mailings before mailed FIT, appear to contribute to effectiveness; 2) invitation letters should be brief and easy to read, and the signatory should be tailored based on setting; 3) instructions for FIT completion should be simple and address challenges that may lead to failed laboratory processing, such as notation of collection date; 4) reminders delivered to initial noncompleters should be used to increase the FIT return rate; 5) data infrastructure should identify eligible patients and track each step in the outreach process, from primer delivery through abnormal FIT follow-up; 6) protocols and procedures such as navigation should be in place to promote colonoscopy after abnormal FIT; 7) a high-quality, 1-sample FIT should be used; 8) sustainability requires a program champion and organizational support for the work, including sufficient funding and external policies (such as quality reporting requirements) to drive commitment to program investment; and 9) the cost effectiveness of mailed FIT has been established. Participants concluded that mailed FIT is an effective and efficient strategy with great potential for increasing colorectal cancer screening in diverse health care settings if more widely implemented.

Beyond Neyman-Pearson: E-values enable hypothesis testing with a data-driven alpha.

A standard practice in statistical hypothesis testing is to mention the P-value alongside the accept/reject decision. We show the advantages of mentioning an e-value instead. With P-values, it is not clear how to use an extreme observation (e.g. [Formula: see text]) for getting better frequentist decisions. With e-values it is straightforward, since they provide Type-I risk control in a generalized Neyman-Pearson setting with the decision task (a general loss function) determined post hoc, after observation of the data-thereby providing a handle on "roving [Formula: see text]'s." When Type-II risks are taken into consideration, the only admissible decision rules in the post hoc setting turn out to be e-value-based. Similarly, if the loss incurred when specifying a faulty confidence interval is not fixed in advance, standard confidence intervals and distributions may fail, whereas e-confidence sets and e-posteriors still provide valid risk guarantees. Sufficiently powerful e-values have by now been developed for a range of classical testing problems. We discuss the main challenges for wider development and deployment.

Democratizing social scientists' impact on federal policy: Using the evidence act to help government and ourselves.

It is common for social scientists to discuss the implications of our research for policy. However, what actions can we take to inform policy in more immediate and impactful ways, regardless of our existing institutional affiliations or personal connections? Focusing on federal policy, I suggest that the answer requires understanding a basic coordination problem. On the government side, the Foundations of Evidence-based Policymaking Act (2018) requires that large federal agencies pose, communicate, and answer research questions related to their effects on people and communities. This advancement has opened the black box of federal agency policy priorities, but it has not addressed capacity challenges: These agencies often do not have the financial resources or staff to answer the research questions they pose. On the higher education side, we have more than 150,000 academic social scientists who are knowledge producers and educators by training and vocation. However, especially among those in disciplinary departments, or those without existing institutional or personal connections to federal agencies, we often feel locked out of federal policymaking processes. In this article, I define the coordination problem and offer concrete actions that the academic and federal government communities can take to address it. I also offer leading examples of how academics and universities are making public policy impact possible in multiple governmental spheres. I conclude by arguing that both higher education institutions and all levels of government can do more to help academic social scientists put our knowledge to work in service of the public good.

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.

Follow the trail: Using insights from the growth of palliative care to propose a roadmap for cancer rehabilitation.

Despite research explicating the benefits of cancer rehabilitation interventions to optimize physical, social, emotional, and vocational functioning, many reports document low rates of referral to and uptake of rehabilitation in oncology. Cancer rehabilitation clinicians, researchers, and policy makers could learn from the multidisciplinary specialty of palliative care, which has benefited from a growth strategy and has garnered national recognition as an important and necessary aspect of oncology care. The purpose of this article is to explore the actions that have increased the uptake and integration of palliative care to yield insights and multimodal strategies for the development and growth of cancer rehabilitation. After examining the history of palliative care and its growth, the authors highlight 5 key strategies that may benefit the field of cancer rehabilitation: 1) stimulating the science in specific gap areas; 2) creating clinical practice guidelines; 3) building clinical capacity; 4) ascertaining and responding to public opinion; and 5) advocating for public policy change. Coordinated and simultaneous advances on these 5 strategies may catalyze the growth, utilization, and effectiveness of patient screening, timely referrals, and delivery of appropriate cancer rehabilitation care that reduces disability and improves quality of life for cancer survivors who need these services.

Equitably improving outcomes for cancer survivors and supporting caregivers: A blueprint for care delivery, research, education, and policy.

Cancer care delivery is being shaped by growing numbers of cancer survivors coupled with provider shortages, rising costs of primary treatment and follow-up care, significant survivorship health disparities, increased reliance on informal caregivers, and the transition to value-based care. These factors create a compelling need to provide coordinated, comprehensive, personalized care for cancer survivors in ways that meet survivors' and caregivers' unique needs while minimizing the impact of provider shortages and controlling costs for health care systems, survivors, and families. The authors reviewed research identifying and addressing the needs of cancer survivors and caregivers and used this synthesis to create a set of critical priorities for care delivery, research, education, and policy to equitably improve survivor outcomes and support caregivers. Efforts are needed in 3 priority areas: 1) implementing routine assessment of survivors' needs and functioning and caregivers' needs; 2) facilitating personalized, tailored, information and referrals from diagnosis onward for both survivors and caregivers, shifting services from point of care to point of need wherever possible; and 3) disseminating and supporting the implementation of new care methods and interventions.

Implementing personalized pathways for cancer follow-up care in the United States: Proceedings from an American Cancer Society-American Society of Clinical Oncology summit.

A new approach to cancer follow-up care is necessary to meet the needs of cancer survivors while dealing with increasing volume and provider shortages, knowledge gaps, and costs to both health care systems and patients. An approach that triages patients to personalized follow-up care pathways, depending on the type(s) and level(s) of resources needed for patients' long-term care, is in use in the United Kingdom and other countries and has been shown to meet patients' needs, more efficiently use the health care system, and reduce costs. Recognizing that testing and implementing a similar personalized approach to cancer follow-up care in the United States will require a multipronged strategy, the American Cancer Society and the American Society of Clinical Oncology convened a summit in January 2018 to identify the needed steps to move this work from concept to implementation. The summit identified 4 key strategies going forward: 1) developing a candidate model (or models) of care delivery; 2) building the case for implementation by conducting studies modeling the effects of personalized pathways of follow-up care on patient outcomes, workforce and health care resources, and utilization and costs; 3) creating consensus-based guidelines to guide the delivery of personalized care pathways; and 4) identifying and filling research gaps to develop and implement needed care changes. While these national strategies are pursued, oncology and primary care providers can lay the groundwork for implementation by assessing their patients' risk of recurrence and the chronic and late effects of cancer as well as other health care needs and resources available for care and by considering triaging patients accordingly, referring patients to appropriate specialized survivorship clinics as these are developed, helping to support patients who are capable of self-managing their health, setting expectations with patients from diagnosis onward for the need for follow-up in primary care and/or a survivorship clinic, and improving coordination of care between oncology and primary care.

Brachytherapy: An overview for clinicians.

Brachytherapy is a specific form of radiotherapy consisting of the precise placement of radioactive sources directly into or next to the tumor. This technique is indicated for patients affected by various types of cancers. It is an optimal tool for delivering very high doses to the tumor focally while minimizing the probability of normal tissue complications. Physicians from a wide range of specialties may be involved in either the referral to or the placement of brachytherapy. Many patients require brachytherapy as either primary treatment or as part of their oncologic care. On the basis of high-level evidence from randomized controlled trials, brachytherapy is mainly indicated: 1) as standard in combination with chemoradiation in patients with locally advanced cervical cancer; 2) in surgically treated patients with uterine endometrial cancer for decreasing the risk of vaginal vault recurrence; 3) in patients with high-risk prostate cancer to perform dose escalation and improve progression-free survival; and 4) in patients with breast cancer as adjuvant, accelerated partial breast irradiation or to boost the tumor bed. In this review, the authors discuss the clinical relevance of brachytherapy with a focus on indications, levels of evidence, and results in the overall context of radiation use for patients with cancer.

Integration of Multiple Metabolic Signals Determines Cell Fate Prior to Commitment.

Cell-fate decisions are central to the survival and development of both uni- and multicellular organisms. It remains unclear when and to what degree cells can decide on future fates prior to commitment. This uncertainty stems from experimental and theoretical limitations in measuring and integrating multiple signals at the single-cell level during a decision process. Here, we combine six-color live-cell imaging with the Bayesian method of statistical evidence to study the meiosis/quiescence decision in budding yeast. Integration of multiple upstream metabolic signals predicts individual cell fates with high probability well before commitment. Cells "decide" their fates before birth, well before the activation of pathways characteristic of downstream cell fates. This decision, which remains stable through several cell cycles, occurs when multiple metabolic parameters simultaneously cross cell-fate-specific thresholds. Taken together, our results show that cells can decide their future fates long before commitment mechanisms are activated.

Science, evidence, law, and justice.

For nearly 25 y, the Committee on Science, Technology, and Law (CSTL), of the National Academies of Sciences, Engineering, and Medicine, has brought together distinguished members of the science and law communities to stimulate discussions that would lead to a better understanding of the role of science in legal decisions and government policies and to a better understanding of the legal and regulatory frameworks that govern the conduct of science. Under the leadership of recent CSTL co-chairs David Baltimore and David Tatel, and CSTL director Anne-Marie Mazza, the committee has overseen many interdisciplinary discussions and workshops, such as the international summits on human genome editing and the science of implicit bias, and has delivered advisory consensus reports focusing on topics of broad societal importance, such as dual use research in the life sciences, voting systems, and advances in neural science research using organoids and chimeras. One of the most influential CSTL activities concerns the use of forensic evidence by law enforcement and the courts, with emphasis on the scientific validity of forensic methods and the role of forensic testimony in bringing about justice. As coeditors of this Special Feature, CSTL alumni Tom Albright and Jennifer Mnookin have recruited articles at the intersection of science and law that reveal an emerging scientific revolution of forensic practice, which we hope will engage a broad community of scientists, legal scholars, and members of the public with interest in science-based legal policy and justice reform.

The American Society for Microbiology collaboration with the CDC Laboratory Medicine Best Practices initiative for evidence-based laboratory medicine.

SUMMARYClinical medicine has embraced the use of evidence for patient treatment decisions; however, the evaluation strategy for evidence in laboratory medicine practices has lagged. It was not until the end of the 20th century that the Institute of Medicine (IOM), now the National Academy of Medicine, and the Centers for Disease Control and Prevention, Division of Laboratory Systems (CDC DLS), focused on laboratory tests and how testing processes can be designed to benefit patient care. In collaboration with CDC DLS, the American Society for Microbiology (ASM) used an evidence review method developed by the CDC DLS to develop a program for creating laboratory testing guidelines and practices. The CDC DLS method is called the Laboratory Medicine Best Practices (LMBP) initiative and uses the A-6 cycle method. Adaptations made by ASM are called Evidence-based Laboratory Medicine Practice Guidelines (EBLMPG). This review details how the ASM Systematic Review (SR) Processes were developed and executed collaboratively with CDC's DLS. The review also describes the ASM transition from LMBP to the organization's current EBLMPG, maintaining a commitment to working with agencies in the U.S. Department of Health and Human Services and other partners to ensure that EBLMPG evidence is readily understood and consistently used.

Integrated Perceptual Decisions Rely on Parallel Evidence Accumulation.

The ability to make accurate and timely decisions, such as judging when it is safe to cross the road, is the foundation of adaptive behavior. While the computational and neural processes supporting simple decisions on isolated stimuli have been well characterized, decision-making in the real world often requires integration of discrete sensory events over time and space. Most previous experimental work on perceptual decision-making has focused on tasks that involve only a single, task-relevant source of sensory input. It remains unclear, therefore, how such integrative decisions are regulated computationally. Here we used psychophysics, electroencephalography, and computational modeling to understand how the human brain combines visual motion signals across space in the service of a single, integrated decision. To that purpose, we presented two random-dot kinematograms in the left and the right visual hemifields. Coherent motion signals were shown briefly and concurrently in each location, and healthy adult human participants of both sexes reported the average of the two motion signals. We directly tested competing predictions arising from influential serial and parallel accounts of visual processing. Using a biologically plausible model of motion filtering, we found evidence in favor of parallel integration as the fundamental computational mechanism regulating integrated perceptual decisions.

Implementation of Obesity Science Into Clinical Practice: A Scientific Statement From the American Heart Association.

Obesity is a recognized public health epidemic with a prevalence that continues to increase dramatically in nearly all populations, impeding progress in reducing incidence rates of cardiovascular disease. Over the past decade, obesity science has evolved to improve knowledge of its multifactorial causes, identifying important biological causes and sociological determinants of obesity. Treatments for obesity have also continued to develop, with more evidence-based programs for lifestyle modification, new pharmacotherapies, and robust data to support bariatric surgery. Despite these advancements, there continues to be a substantial gap between the scientific evidence and the implementation of research into clinical practice for effective obesity management. Addressing barriers to obesity science implementation requires adopting feasible methodologies and targeting multiple levels (eg, clinician, community, system, policy) to facilitate the delivery of obesity-targeted therapies and maximize the effectiveness of guideline-driven care to at-need patient populations. This scientific statement (1) describes strategies shown to be effective or promising for enhancing translation and clinical application of obesity-based research; (2) identifies key gaps in the implementation of obesity science into clinical practice; and (3) provides guidance and resources for health care professionals, health care systems, and other stakeholders to promote broader implementation and uptake of obesity science for improved population-level obesity management. In addition, advances in implementation science that hold promise to bridge the know-do gap in obesity prevention and treatment are discussed. Last, this scientific statement highlights implications for health research policy and future research to improve patient care models and optimize the delivery and sustainability of equitable obesity-related care.

Effects of an Intervention to Improve Evidence-Based Care for People With Diabetes and Cardiovascular Disease Across Sex, Race, and Ethnicity Subgroups: Insights From the COORDINATE-Diabetes Trial.

BACKGROUND: Results from the COORDINATE-Diabetes trial (Coordinating Cardiology Clinics Randomized Trial of Interventions to Improve Outcomes - Diabetes) demonstrated that a multifaceted, clinic-based intervention increased prescription of evidence-based medical therapies to participants with type 2 diabetes and atherosclerotic cardiovascular disease. This secondary analysis assessed whether intervention success was consistent across sex, race, and ethnicity. METHODS: COORDINATE-Diabetes, a cluster randomized trial, recruited participants from 43 US cardiology clinics (20 randomized to intervention and 23 randomized to usual care). The primary outcome was the proportion of participants prescribed all 3 groups of evidence-based therapy (high-intensity statin, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, and sodium-glucose cotransporter-2 inhibitor or glucagon-like peptide 1 receptor agonist) at last trial assessment (6 to 12 months). In this prespecified analysis, mixed-effects logistic regression models were used to assess the outcome by self-reported sex, race, and ethnicity in the intervention and usual care groups, with adjustment for baseline characteristics, medications, comorbidities, and site location. RESULTS: Among 1045 participants with type 2 diabetes and atherosclerotic cardiovascular disease, the median age was 70 years, 32% were female, 16% were Black, and 9% were Hispanic. At the last trial assessment, there was an absolute increase in the proportion of participants prescribed all 3 groups of evidence-based therapy in women (36% versus 15%), Black participants (41% versus 18%), and Hispanic participants (46% versus 18%) with the intervention compared with usual care, with consistent benefit across sex (male versus female; Pinteraction=0.44), race (Black versus White; Pinteraction=0.59), and ethnicity (Hispanic versus Non-Hispanic; Pinteraction= 0.78). CONCLUSIONS: The COORDINATE-Diabetes intervention successfully improved delivery of evidence-based care, regardless of sex, race, or ethnicity. Widespread dissemination of this intervention could improve equitable health care quality, particularly among women and minority communities who are frequently underrepresented in clinical trials. REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03936660.

Establishing the Medical Actionability of Genomic Variants.

Actionability is an important concept in medicine that does not have a well-accepted standard definition, nor is there a general consensus on how to establish it. Medical actionability is often conflated with clinical utility, a related but distinct concept. This lack of clarity contributes to practice variation and inconsistent coverage decisions in genomic medicine, leading to the potential for systematic bias in the use of evidence-based interventions. We clarify how medical actionability and clinical utility are distinct and then discuss the spectrum of actionability, including benefits for the person, the family, and society. We also describe applications across the life course, including prediction, diagnosis, and treatment. Current challenges in assessing the medical actionability of identified genomic variants include gaps in the evidence, limited contexts with practice guidelines, and subjective aspects of medical actionability. A standardized and authoritative assessment of medical actionability is critical to implementing genomic medicine in a fashion that improves population health outcomes and reduces health disparities.