Actionable Information Research Articles

Identification of paddy blast disease field images using multi-layer CNN models.

Farmers and agricultural experts can take action on many areas of paddy crop handling and management practices with the use of actionable information from the in-field diagnosis of paddy blast disease. To successfully diagnose the blast disease affecting fifteen different paddy crop varieties, three transfer learning multi-layer convolutional neural network (CNN) models, such as CapsNet, EfficientNet-B7, and ResNet-50, are presented in this paper. The field images of blast disease are captured and classified based on disease severity levels, such as low, medium, high, and severe. The study employing the CapsNet model with adataset consisting of a total of 20,000 labeled images demonstrated significant results with a testing efficiency of 90.79% and a validation efficiency of 93.29%. The ResNet-50 and EfficientNet-B7 models have yielded average testing efficiencies of 85.10% and 88.72%, respectively. On the held out blast disease affected paddy field image dataset, the CapsNet model outperformed the EfficientNet-B7 and ResNet-50 CNN models in terms of both classification efficiency and computational efficiency.

Utilising clinical parameters to improve the selection of nerve biopsy candidates.

Peripheral nerve biopsy is a valuable final diagnostic tool; however, histopathological results can be non-diagnostic. We aim to identify quality improvement measures by evaluating the pre-biopsy assessment and diagnostic yield of specific histopathological diagnosis. This was a retrospective study based on 10 years of experience with peripheral nerve biopsies at a single centre. Clinical data were obtained regarding pre-biopsy history, examination, serum and cerebrospinal fluid (CSF) investigations, neurophysiology and peripheral nerve imaging. Based upon a histopathological outcome, patients were grouped into vasculitis, granulomatous and infiltrative (diagnostic) group, or a comparison group of non-specific axonal neuropathy and normal (non-specific/normal) group. From a cohort of 64 patients, 21 (32.8%) were included in the diagnostic group and 30 (46.9%) in the non-specific/normal group. Clinical parameters associated with the diagnostic group were shorter history (mean 10.2 months vs 38.1), stepwise progression (81% vs 20%), neuropathic pain (85.7% vs 56.7%), vasculitic rash (23.8% vs 0%), mononeuritis multiplex (57.1% vs 10%), asymmetry (90.5% vs 60%), raised white cell count (47.6% vs 16.7%), myeloperoxidase antibody (19.1% vs 0%) and abnormal peripheral nerve imaging (33.3% vs 10%). Selection of patients undergoing nerve biopsy requires careful consideration of clinical parameters, including peripheral nerve imaging. Several quality improvement measures are proposed to improve yield of clinically actionable information from nerve biopsy.

Operations (management) warp speed: Rapid deployment of hospital‐focused predictive/prescriptive analytics for the COVID‐19 pandemic

At the onset of the COVID‐19 pandemic, hospitals were in dire need of data‐driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state‐ and national‐level policy decisions, with little availability of actionable information to support tactical and operational decision‐making and execution at the hospital level. To fill this gap, we developed a multi‐method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high‐impact predictive and prescriptive analytics in a time‐sensitive, dynamic, data‐limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission‐critical, actionable information for individual hospitals. Our framework forecasts time‐varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital‐dependent, time‐varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health.

ASSESSING THE FORECASTING BY MSMS FOR MEDICAL STORES MANAGEMENT OF A TERTIARY CARE TEACHING HOSPITAL

Medical Stores management is an important aspect in healthcare delivery to patients. Inventory in a medical store is one of the vital components of the current assets. Excess holdings of inventory may not only increase the cost but may also lead to wastage. Effective and efcient management of inventory is an integral part of supply chain. Improper use of pharmaceutical products or shortage of medicine not only cause nancial loss but may also affect the patients adversely. Contrary to the traditional techniques of managing inventory, Articial Intelligence (AI) is gaining credibility in making the process more effective and efcient. AI is the application of computer program that demonstrates action like a human being, learns from experience, gets new input and processes big data by reasoning. It can acquire large amount of data and create rules for turning the data into actionable information. This study aims to assess the effective implementation of the medical stores management system (MSMS) by correlating the forecasted Monthly Maintenance Figure (MMF) and the actual consumption pattern of the consumables used in the hospital in the 3 consecutive years using Root mean Square error (RMSE). RMSE was used as a measures for evaluating the quality of predictions. As RMSE is used to measure the deviation from the expected and actual values, the values closer to 0 are considered better. The RMSE was found to be 3.8 in 2020, 3.33 in 2021 and reduced to 1.33 in 2022 forecast with a correlation coefcient of 0.75(p-value= 0.001 x 10-20).

Characterization of the immune environment in pregnancy-associated breast cancer

Pregnancy-associated breast cancer (PrBC) is a rare and clinically challenging condition. Specific immune mechanisms and pathways are involved in maternal–fetal tolerance and tumor-host immunoediting. The comprehension of the molecular processes underpinning this immune synergy in PrBC is needed to improve patients' clinical management. Only a few studies focused on the immune biology of PrBC and attempted to identify bona fide biomarkers. Therefore, clinically actionable information remains extremely puzzling for these patients. In this review article, we discuss the current knowledge on the immune environment of PrBC, in comparison with pregnancy-unrelated breast cancer and in the context of maternal immune changes during pregnancy. A particular emphasis is given to the actual role of potential immune-related biomarkers for PrBC clinical management.

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A Big Data framework for actionable information to manage drinking water quality

Abstract Water utilities collect vast amounts of data, but they are stored and utilised in silos. Machine learning (ML) techniques offer the potential to gain deeper insight from such data. We set out a Big Data framework that for the first time enables a structured approach to systematically progress through data storage, integration, analysis, and visualisation, with applications shown for drinking water quality. A novel process for the selection of the appropriate ML method, driven by the insight required and the available data, is presented. Case studies for a water utility supplying 5.5 million people validate the framework and provide examples of its use to derive actionable information from data to help ensure the delivery of safe drinking water.

Beyond A1C: exploring continuous glucose monitoring metrics in managing diabetes.

Hemoglobin A1c (HbA1c) has long been considered a cornerstone of diabetes mellitus (DM) management, as both an indicator of average glycemia and a predictor of long-term complications among people with DM. However, HbA1c is subject to non-glycemic influences which confound interpretation and as a measure of average glycemia does not provide information regarding glucose trends or about the occurrence of hypoglycemia and/or hyperglycemia episodes. As such, solitary use of HbA1c, without accompanying glucose data, does not confer actionable information that can be harnessed to guide targeted therapy in many patients with DM. While conventional capillary blood glucose monitoring (BGM) sheds light on momentary glucose levels, in practical use the inherent infrequency of measurement precludes elucidation of glycemic trends or reliable detection of hypoglycemia or hyperglycemia episodes. In contrast, continuous glucose monitoring (CGM) data reveal glucose trends and potentially undetected hypo- and hyperglycemia patterns that can occur between discrete BGM measurements. The use of CGM has grown significantly over the past decades as an ever-expanding body of literature demonstrates a multitude of clinical benefits for people with DM. Continually improving CGM accuracy and ease of use have further fueled the widespread adoption of CGM. Furthermore, percent time in range correlates well with HbA1c, is accepted as a validated indicator of glycemia, and is associated with the risk of several DM complications. We explore the benefits and limitations of CGM use, the use of CGM in clinical practice, and the application of CGM to advanced diabetes technologies.

Evaluation of Preferred Language and Timing of COVID-19 Vaccine Uptake and Disease Outcomes

Beyond traditional race and ethnicity demographic characteristics, additional discrete data variables are needed for informed health interventions in the US. To examine whether COVID-19 vaccine uptake patterns and associated disease outcomes differ among language preference groups. A cohort study of 851 410 individuals aged 18 years or older in a large multispecialty health system in Minnesota and western Wisconsin was conducted between December 15, 2020, and March 31, 2022. Self-identified language preference and limited English proficiency (LEP) as measured by interpreter need were used to create subgroups using US census categories and attention to capture languages known to represent refugee groups. The primary outcome was COVID-19 vaccination uptake rates and time to first vaccine. Secondary outcomes were rates of COVID-19-associated hospitalization and death. Most of the 851 410 participants (women, 493 910 [58.0%]; median age, 29 [IQR, 35-64] years) were US-born English speakers; 7.5% were born in other countries, 4.0% had a language preference other than English (LPOE), and 3.0% indicated LEP as measured by interpreter need. Marked temporal clusters were observed for COVID-19 vaccination uptake, hospitalizations, and deaths associated with primary series vaccine eligibility, booster availability, and COVID-19 variants. Delayed first-dose vaccine was observed with LPOE (hazard ratio [HR], 0.83; 95% CI, 0.82-0.84) and interpreter need (HR, 0.81; 95% CI, 0.80-0.82) compared with those with English language preference and proficiency. Patients with LPOE were approximately twice as likely to be hospitalized (rate ratio [RR], 1.85; 95% CI, 1.63-2.08) or die (RR, 2.13; 95% CI, 1.65-2.69). Patients with LEP experienced even higher rates of hospitalization (RR, 1.98; 95% CI, 1.73-2.25) and COVID-19-associated death (RR, 2.32; 95% CI, 1.79-2.95). Outcomes varied for individual language preference groups. In this study, delayed time to first-dose vaccine was associated with increased COVID-19 hospitalization and death rates for specific LPOE and LEP groups. The findings suggest that data collection of language preference and interpreter need provides actionable health intervention information. Standardized system-level data collection, including at a national level, may improve efficient identification of social groups with disproportionate health disparities and provide key information on improving health equity in the US.

A model for estimating costs and benefits of new vaccine technologies from the perspective of both buyers and sellers.

Although vaccination is widely considered one of the most cost-effective health interventions available, global coverage rates for many vaccines remain lower than necessary for disease elimination and eradication. New vaccine technologies can play an important role in addressing barriers to vaccination and increasing coverage rates. To identify and prioritize vaccine technology investments, decision makers must be able to compare the overall costs and benefits of each investment option. While these data points may exist, they are often confined to silos. Decision makers would benefit from a model that synthesizes this broad range of data and provides clear and actionable information. To facilitate vaccine investment, purchasing and deployment decisions, we developed a systematic and transparent cost-benefit model that estimates the value and risk of a given investment scenario from the perspective of both "buyers" (e.g., global donors, country governments) and "sellers" (e.g., developers, manufacturers) of vaccines. This model, which can be used to evaluate scenarios related to a single vaccine presentation or a portfolio of vaccine presentations, leverages our published approach for estimating the impact of improved vaccine technologies on vaccination coverage rates. This article presents a description of the model and provides an illustrative example application to a portfolio of measles-rubella vaccine technologies currently under development. Although the model is generally applicable to organizations involved in vaccine investment, manufacturing or purchasing, we believe it may be particularly useful to those engaged in vaccine markets that rely strongly on funding from institutional donors.

Abstract 4916: Spatial analysis of local drug induced changes in tumor microenvironment predicts effective treatment combinations in breast cancer

Abstract Anticancer therapeutics primarily designed to target tumor intrinsic mechanisms may also affect components of tumor microenvironment (TME) - immune cells and non-immune stroma. Recent literature reenforces the concept that complex interactions between drugs, neoplastic cells and cells of TME determine the efficacy of anticancer therapies. Systems understanding of these interactions can serve to predict effective treatment combinations simultaneously attacking tumor cell vulnerabilities, enhancing immune surveillance, and mitigating stromal mediators of resistance. The key challenge is to find such TME-modulating combinations in a fast and more informative way. We have developed an integrated analytical platform termed Multiplex Implantable Microdevice Assay (MIMA) to rapidly decompose cancer complexity in drug response and find biomarkers with predictive value for combination therapy efficacy including immunotherapy efficacy. The system deploys a (i) miniaturized implantable microdevice for localized intratumoral drug delivery and (ii) multiplex immunostaining to measure 30+ proteins in single cells at each drug well. Computational analyses of local drug-induced changes provide information about the composition, functional state and spatial cell organization of the tumor and associated TME ultimately bringing new insights into drug mechanisms of action. We used MIMA in genetically engineered mouse models of breast cancer to evaluate effects of five targeted anticancer agents (olaparib, palbociclib, venetoclax, panobinostat, lenvatinib) and two chemotherapies (doxorubicin, paclitaxel) and predicted synergistic antitumor effects with anti-PD-1, anti-CD40, anti-CSF1R immunotherapies and vasculature modulating agents. Some of the most effective combinations were not reported before. A pan-HDAC inhibitor, panobinostat, that synergized with anti-PD-1, induced immunogenic cell death and infiltration of antigen presenting neutrophils. Further longitudinal spatial analyses revealed that mechanisms of resistance co-emerged with these response phenotypes and became prominent over time. We measured fibroblast, protumorigenic macrophage and cytotoxic B cell recruitment associated with heavy deposition of collagen VI, increased immune suppression and emergence of invasive and cancer stem cells. Combination with stroma modulating agent, losartan, improved the efficacy of panobinostat/anti-PD-1 in systemic studies implying that normalization of non-immune stroma might favorably alter aspects of TME for immune and targeted therapy efficacy. All in all, MIMA may represent a new approach to predict effective combination regimens for individual cancer patients. Extended MIMA use and computational modeling of the spatial cell patterns could provide actionable information for development of effective drug doses and schedules. Citation Format: Zuzana Tatarova, Dylan C. Blumberg, Gordon B. Mills, Lisa M. Coussens, Oliver Jonas, Joe W. Gray. Spatial analysis of local drug induced changes in tumor microenvironment predicts effective treatment combinations in breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4916.

Delivering the latest global water resource simulation results to the public

Increasing demands from governments and businesses for accurate and actionable information regarding water security have led to the development of various interactive websites that provide water risk indicators. However, these websites often lack adequate descriptions of the sources and limitations of their datasets and failure to take any uncertainty associated with water risk indicators into account can have serious consequences. Furthermore, websites that only provide data for specific time points, such as mid-century and end-of-century, may be less useful. To address these limitations, we present the H08 Water Risk Tool, a scientifically rigorous platform designed to assess historical and future water resources.The H08 Water Risk Tool incorporates advanced hydrological simulations that utilize the latest modeling techniques, input data, and parameter calibration methods. Our validation indicates that the temporal variation and spatial distribution characteristics of the historical water risk indicators produced by the H08 Water Risk Tool agree with previous observations. This platform provides the public with high-quality water risk indicators, as well as the tools necessary to interpret these indicators. To ensure complete transparency, the H08 Water Risk Tool uses open-source tools and provides detailed information on its methodology and assumptions.Despite the ongoing Water Action Decade initiative launched by the United Nations, avoiding a global water crisis will require considerable work. The H08 Water Risk Tool will be a defining asset providing comprehensive and realistic assessments of water scarcity, both current and future, as well as helping to prioritize adjustments to climate change.

A Weather-Ready Nation for All? The Demographics of Severe Weather Understanding, Reception, and Response

Abstract NOAA’s National Weather Service (NWS) provides forecasts, warnings, and decision support to the public for the protection of life and property. The NWS Weather-Ready Nation model describes the process of applying weather information to achieve societal value. However, it is not clear how different racial and socioeconomic groups across the United States receive, understand, and act upon the weather information supplied under this model. There may be barriers that keep important, lifesaving information from the populations at the highest risk of severe weather impacts. This paper estimates the extent of racial and socioeconomic disparities in severe weather risk information reception, comprehension, response, and trust, as well as severe weather preparedness and risk perceptions in the United States. We use data from the University of Oklahoma’s Severe Weather and Society Survey, which is annually completed by a sample of 3000 U.S. adults (age 18+) that is designed to match the characteristics of the U.S. population. We pool data over four years (2017–20) to provide reliable severe weather risk prevalence statistics for adults by race, ethnicity, and socioeconomic characteristics. As a robustness check, we supplement this information with data from the annual FEMA National Household Survey. We find that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. These findings suggest that NWS and their partners should adjust their communication strategies to ensure all populations receive and understand actionable severe weather information. Significance Statement It is crucial that severe weather risk communication is received, appropriately interpreted, and trusted by all communities—especially the most vulnerable. Past research has not explained how different racial and socioeconomic groups receive, understand, and act upon NWS forecasts and warnings. This study finds that racial and socioeconomic groups receive, understand, trust, and act upon severe weather information differently. Risk communication strategies should be adjusted to eliminate barriers that keep important, lifesaving information from vulnerable populations.

Leveling Up Sport Management Education: Gamification in the Classroom

In line with trends in sport management education that have encouraged a transition from traditional forms of passive and depersonalized learning to active and motivated learning, this essay draws on theoretical and applied insight to provide sport management educators with actionable information related to gamification. In educational contexts, gamification involves using game elements such as narratives, teams, and badges in the classroom as a way to support students’ intrinsic motivation and basic psychological needs (i.e., autonomy, competence, relatedness). This essay presents a case for gamification as a high-impact pedagogical approach that can help sport management educators replicate the global, complicated, and dynamic nature of the sport industry, thus creating more authentic, engaging, and influential experiences for students. Accordingly, this essay outlines gamification in education, discusses game elements and design, and provides a thorough description of a gamified sport psychology course. It concludes with future considerations and key takeaways for sport management educators.

The Performativity of AI-powered Event Detection: How AI Creates a Racialized Protest and Why Looking for Bias Is Not a Solution

This article builds a theoretical framework with which to confront the racializing capabilities of artificial intelligence (AI)-powered real-time Event Detection and Alert Creation (EDAC) software when used for protest detection. It is well-known that many AI-powered systems exacerbate social inequalities by racializing certain groups and individuals. We propose the feminist concept of performativity, as defined by Judith Butler and Karen Barad, as a more comprehensive way to expose and contest the harms wrought by EDAC than that of other “de-biasing” mechanisms. Our use of performativity differs from and complements other Social Studies of Science and Technology (STS) work because of its rigorous approach to how iterative, citational, and material practices produce the effect of race. We focus on Geofeedia and Dataminr, two EDAC companies that claim to be able to “predict” and “recognize” the emergence of dangerous protests, and show how their EDAC tools performatively produce the phenomena which they are supposed to observe. Specifically, we argue that this occurs because these companies and their stakeholders dictate the thresholds of (un)intelligibility, (ab)normality, and (un)certainty by which these tools operate and that this process is oriented toward the production of commercially actionable information.

Barriers to oral health care for transgender and gender nonbinary populations

BackgroundDespite health disparities and barriers to medical care being well documented in the literature, transgender and gender nonbinary (TGNB) people’s experiences and expectations with regard to oral health care remain understudied. The authors examined gender identity–related factors influencing experiences in the dental setting, aspects of subjective oral health, and avoidance of oral health care. MethodsOne-hundred eighteen TGNB people aged 13 through 70 years completed a 32-item questionnaire designed for this study. Data analysis relied on descriptive methods and bivariate comparisons using a conventional P < .05 statistical significance criterion. Qualitative description analysis was used to identify emerging themes from responses to an open-ended question. ResultsOne-third of participants reported misgendering (that is, had been addressed by their incorrect name and pronouns in the dental setting). Although refusal of oral health care was rare in this sample of TGNB participants, more than one-half felt that their usual source of oral health care was not equipped to provide gender-appropriate care. Participants’ avoidance due to gender identity was significantly associated with measures of self-reported suboptimal oral health. Common themes related to participants’ oral health care experiences included gender insensitivity, awkward interactions, avoidance of care, and lack of gender-affirming providers. ConclusionsDiscrepancies between TGNB patients’ expectations and actual experiences suggest that their needs are often unmet in the dental setting, possibly contributing to gender identity–associated dental avoidance and oral health disparities. Practical ImplicationsAlthough these results need to be verified in larger and more diverse samples, they provide actionable information for improvement to this population’s oral health and management.

Common sources and needs of weather information for rice disease forecasting and management in coastal Bangladesh

Rice farmers in coastal Bangladesh are severely affected by climate change. Climate variability and change impact crop production in numerous ways, including by changing occurrences of different rice diseases. Weather and climate information services (WCIS) can assist farmers in improving agricultural decision-making, in relation to rice disease management. Currently, however, farmers in coastal Bangladesh have limited access to accurate, timely, and location-specific information on weather and climate. To design actionable information services, it is important to know the types of information that farmers need. With this in mind, the current research addressed three questions: (i) What types of weather information do coastal farmers need to forecast rice diseases? (ii) What types of weather information are currently available to coastal farmers? (iii) How does weather information play a role in rice disease management-related decision-making? Results indicate that coastal rice farmers have access to weather and climate information services mainly through personal experiences, neighbouring farmers, agricultural extension agents, local knowledge and input dealers. During interviews, farmers indicated that they mainly needed information on temperatures, rainfall, relative humidity, cloud cover, fog and wind information to predict rice disease outbreaks sufficiently in advance. Raising awareness about application of climate information services in rice disease management and building the capacity of farmers through training and education is vital to address major challenges faced by the coastal farmers in accessing weather information. We conclude that the farmers need more accurate, timely, location-specific, trustworthy and actionable weather information services to forecast and manage rice diseases.

Surfactant Micellar and Vesicle Microenvironments and Structures under Synthetic Organic Conditions.

Fluorescence lifetime imaging microscopy (FLIM) reveals vesicle sizes, structures, microenvironments, reagent partitioning, and system evolution with two chemical reactions for widely used surfactant-water systems under conditions relevant to organic synthesis, including during steps of Negishi cross-coupling reactions. In contrast to previous investigations, the present experiments characterize surfactant systems with representative organohalide substrates at high concentrations (0.5 M) that are reflective of the preparative-scale organic reactions performed and reported in water. In the presence of representative organic substrates, 2-iodoethylbenzene and 2-bromo-6-methoxypyridine, micelles swell into emulsion droplets that are up to 20 μm in diameter, which is 3-4 orders of magnitude larger than previously measured in the absence of an organic substrate (5-200 nm). The partitioning of reagents in these systems is imaged through FLIM─demonstrated here with nonpolar, amphiphilic, organic, basic, and oxidative-addition reactive compounds, a reactive zinc metal powder, and a palladium catalyst. FLIM characterizes the chemical species and/or provides microenvironment information inside micelles and vesicles. These data show that surfactants cause surfactant-dictated microenvironments inside smaller micelles (<200 nm) but that addition of a representative organic substrate produces internal microenvironments dictated primarily by the substrate rather than by the surfactant, concurrent with swelling. Addition of a palladium catalyst causes the internal environments to differ between vesicles─information that is not available through nor predicted from prior analytical techniques. Together, these data provide immediately actionable information for revising reaction models of surfactant-water systems that underpin the development of sustainable organic chemistry in water.

Supporting Humanitarian Crisis Decision Making with Reliable Intelligence Derived from Social Media Using AI

Abstract Recent advances in the field of artificial intelligence bring promising new capabilities that can substantially improve our ability to manage complex and evolving situations in the face of uncertainty. Humanitarian crises exemplify such situations, and the pervasiveness of social media renders it one of the most abundant sources of real-time information available. However, it is quite a difficult task to condense a body of social media posts into useful information quickly. In this paper we consider the challenge of using social media reports to provide a reliable, real-time situational awareness in the management of humanitarian crises. Effectively addressing this challenge requires extracting only the relevant information out of text and images in individual social media posts, fusing this information together into actionable information points for decision makers, and providing an assessment of the trustworthiness of this information. We propose a general solution framework and discuss a system developed in collaboration with NATO which combines state-of-the-art deep learning, natural language processing, computer vision, and information fusion models to provide a reliable, actionable, real-time situational awareness for supporting decision making in humanitarian crisis logistics. In addition to the technical approach, we also discuss important practical aspects of this project including the development and validation process, challenges encountered along the way, and key lessons learned.

Association Between Nociplastic Pain and Pain Severity and Impact in Women With Chronic Pelvic Pain

Exploring the relationship between nociplastic pain and the severity and impact of pelvic pain symptoms could lend insight into the heterogeneous symptom presentation and treatment response that complicates management of chronic pelvic pain. In this prospective cross-sectional study, we sought to evaluate relationships between degree of nociplastic pain, measured by the Fibromyalgia (FM) Survey Score, and multiple aspects of the chronic pelvic pain (CPP) experience, including severity, frequency, tenderness during pelvic myofascial exam, interference with daily life, and high-impact pain. The study included 303 women who presented to a tertiary referral clinic for chronic pelvic pain and endometriosis. Multiple measures of pelvic pain, including pain severity, frequency, interference, pelvic myofascial pain, and high-impact pain were examined in General Linear Models with FM Survey Score as the primary predictor of interest in models controlling for endometriosis, surgical history, use of opioids, body mass index, and patient age. Higher level of nociplastic pain was associated with greater pelvic pain severity, frequency, interference, and pelvic myofascial pain (all P < .05). For all models, degree of nociplastic pain was more strongly associated with pain outcomes than the presence of endometriosis, and use of opioids was the only stronger predictor of worse pain outcomes. The likelihood of high impact pain increased 7% for each additional point on the FM Survey Score. Degree of nociplastic pain was robustly associated with severity, frequency, and impact of pelvic pain, and was independent of the presence of endometriosis, history of surgical procedures for pelvic pain, age, and BMI.Trial registration: not applicable PerspectiveThis article evaluates the impact of nociplastic pain on symptoms and functional status in chronic pelvic pain. These findings raise the possibility that a simple screening tool for nociplastic pain might provide clinically actionable information without the need for deep neurobiological phenotyping and may inform development of personalized management strategies.