bluuebunny/crossref_metadata_2025_split huggingface dataset:
Awareness of racial/ethnic inequities in time to treatment among breast cancer providers.
Authors: Cohen Inessa , Canavan Maureen , Jacob Alexandra , Sondhi Arjun , Silver Heather , Bramwell Lindsay Gaido , Lustberg Maryam B. , Adelson Kerin B. 
Published: October 2023

584 Background: Cancer patients who self-identify as Black and Latinx are more likely to receive delays in care compared with Non-Latinx White patients. Research suggests that while providers are aware of racial/ethnic inequities in access to treatment at a national-level, they fail to recognize these differences at the hospital- or individual-level. This phenomenon is not well understood and has yet to be studied among oncologists. We sought to evaluate awareness of national and local time-to-treatment inequities in breast cancer providers. Methods: We conducted an educational intervention at the Smilow Cancer Hospital, the clinical arm of the Yale Cancer Center, in February 2023. Breast cancer providers (including breast surgeons, medical oncologists, radiation oncologists, breast radiologists, and advanced practice nurses) completed an online survey that assessed their understanding of treatment inequities at three levels (national, hospital, and individual) before and after they watched a 20-minute live presentation. The presentation included real-world national and breast-team specific data on differences in time-to-treatment metrics by race/ethnicity from the Flatiron electronic health record-derived de-identified database. Differences between surveys were evaluated with Fisher’s exact tests. Results: Among 18 providers, most were female (78%), Non-Latinx White (83%), had an average of 15 years of experience (SD=10), and half reported at least 10-25% of the patients they care for were non-White. Awareness of race/ethnicity as a factor in influencing differences in oncological care increased significantly (33% to 77 %, p = 0.02) pre-to-post intervention at the hospital-level but did not significantly change at the national- (78% to 83%, p > 0.05) or individual- (17% to 28%, p > 0.05) level. Providers identified adverse social determinants of health, such as housing insecurity, poor employment conditions, and food insecurity, as the main cause of inequities both nationally and within their disease team. On average, at the post-survey, most providers agreed that patient factors (62%) rather than provider factors (30%) were driving differences in oncological care metrics. However, they reported that larger systems, such as healthcare agencies, government, and providers (all 89%), rather than patients, were responsible for reducing these inequities. Conclusions: In this pilot study, a brief educational intervention increased awareness of local hospital-level factors, but not national- and individual-level factors, associated with racial/ethnic inequities in time to cancer treatment. As the oncology field continues to address inequities in care, it is crucial for providers to not only acknowledge the existence of these inequities but also to establish successful interventions to reduce these inequities within their own disease team.
Development and initial evaluation of a visualization tool for multidisciplinary treatment discussion of patients with pancreatic cancer.
Authors: Fouladi Roxanna , Korah Bobby , Herman Joseph M. , King Daniel , Deperalta Danielle , Pinto Danielle , Weiss Matthew John , Lowy Andrew M. , Sarno Shirley , Soldano Debbie A. , Stricker Carrie Tompkins 
Published: October 2023

585 Background: Pancreatic cancer is the third leading cause of cancer death in the United States; it is a complex disease requiring coordinated patient care. Multidisciplinary tumor boards (MTB) are today's gold standard in pancreatic cancer but are frequently criticized for being time-intensive and inefficient. To address this issue, Cancer Collective and Hive Networks have developed a novel patient-information visualization tool designed to improve the communication and efficiency of multidisciplinary tumor board meetings. This pilot study investigated the patient-information visualization tool's usability, efficiency, and feasibility in pancreatic cancer tumor board meetings. Methods: A prospective multi-center pilot study was conducted at Northwell Health (a comprehensive community cancer program [NCCCP] in the Northeastern United States) and the University of California San Diego (an NCI-designated comprehensive cancer center in [NCCC] in the Southwestern United States. The feasibility of the tool was assessed by measuring the percentage of MTB cases discussed that used the patient-information visualization tool. The usability and efficiency of the tool were assessed via Likert scale questions. Acceptable usability and effectiveness were defined as a 70% score or higher. Additionally, MTB participants were queried for input on design improvements for future iterations and expected future directions of tool use. Results: The patient-information visualization tool was rolled out on 07/22/2022 at the NCCP and NCCC. From 07/22/2022-05/31/2023, 170 (92%) MTB cases were reviewed at the NCCP. From 07/22/2022-05/31/2023, 74 cases (100%) of MTB cases were reviewed using the patient-information visualization tool at the NCCP. The NCCP (n=9) mean scores for usability and efficiency were 4.31 (86%) and 4.17 (83%) respectively. The NCCC (n=6) mean scores for usability and efficiency were 4.04 (81%) and 3.96 (79%) respectively. Qualitative feedback, reported on survey open-ended questions, included suggested enhancements to improve the tool’s usability, such as leveraging data automation to reduce data entry burden, improving the user interface for ease of data visualization, and adding additional data elements (e.g., specific procedure and appointment details) to improve decision-making utility. Conclusions: Implementing the novel digital dashboard to visualize pancreatic cancer patient data was highly feasible, usable, and efficient. Future directions of tool use include refining based on qualitative feedback from end users and planning for data automation and interoperability with the EMR.
Multi-task machine learning of the electronic medical record to predict future symptoms among patients with cancer.
Authors: Yuan Baijiang , Kabir Muammar , He Kevin , Grant Benjamin , Narine Sharon , Ajaj Rami , Zhan Luna Jia , Fawzy Aly , Xu Janine , Zhang Yuhua , Yu Vivien , French Conor , Xu Wei , Krishnan Rahul G. , Gallinger Steven , Krzyzanowska Monika K. , Truong Tran , Liu Geoffrey , Grant Robert C 
Published: October 2023

587 Background: Cancer and its treatments often cause symptoms. Automated warning systems could mitigate symptoms by alerting healthcare teams and enabling personalized preventative interventions. We developed a general-purpose longitudinal system for predicting symptomatic deterioration among outpatients undergoing intravenous systemic anti-cancer therapy. Methods: Patients treated for aerodigestive cancers at the Princess Margaret Cancer Centre were randomly divided into development and testing cohorts. For each treatment, machine learning was applied to preceding electronic medical record (EMR) data to predict patient-reported symptom deterioration, defined as at least a four point worsening on the Edmonton Symptom Assessment Scale. Features included diagnostic and treatment characteristics, laboratory tests, and patient-reported symptoms. Single-task (e.g., LASSO and XGboost) and multi-task (e.g., temporal CNNs, LSTM and Transformer) models were trained, tuned, and evaluated based on discrimination, calibration, and net benefit. Results: The cohort consisted of 3,998 patients who underwent 45,904 treatment sessions, with data across 400 features. Among these patients, 1,547 (38.6%) were female; median age was 64.0 (interquartile range 13.0). The most common diagnoses were lung (1,505, 37.6%), head and neck (696, 17.4%), and pancreatic cancers (685, 17.1%). The best model, a multi-task transformer, predicted symptom deterioration with an AUROC range of 0.732-0.822, marking a 1.4-6.2% improvement over the best single-task model. At a 10% alert rate, treatments associated with alerts would be enriched 4-13 fold for symptom deterioration (P<0.001). The system was calibrated and would provide a net benefit across a wide range of threshold probabilities in decision curve analysis. Conclusions: Longitudinal general-purpose multi-task machine learning systems trained using EMR data can accurately predict a wide range of symptoms. Based on these results, automated warning systems for symptoms should be implemented and evaluated in real-time clinical practice to guide preventative interventions.[Table: see text]
Clinical pharmacist prescription review and modification for oral antineoplastic medications at an academic medical center.
Authors: Kaymakcalan Marina D. , Thomas Jenafer , Nowakowski Kristin , Shukla Shital , Le Anne C. , Cibotti Christina , Syngal Monica , Doolin Jim W 
Published: October 2023

64 Background: Collaboration between oncology providers and pharmacy teams has improved the safety of oral antineoplastic (OA) prescribing, but opportunities remain for improvements in care delivery. To this end, starting in March 1, 2021, the Dana Farber Cancer Institute Specialty Pharmacy implemented a new workflow - clinical verification of new specialty prescriptions by dedicated OA clinical pharmacists (OAPs) prior to prescription processing. To reduce the impact of initial prescription errors on efficiency and safety, one component of clinical verification was to expand the purview of OAPs to make certain modifications to prescriptions that could be automatically processed without requiring prescriber re-signing. Automatically processed prescription modifications include corrections to quantity or days supply, indication of cycle length or days on/off therapy, and clarification of directions. Here we report on the impact of this portion of the clinical verification program. Methods: OAP prescription modification (OAP-PM) was introduced on November 1, 2022. To assess the impact on efficiency, we conducted a pre-post analysis of the proportion of prescription modifications taking > 60 minutes to complete for 6 months before and after OAP-PM introduction. Using prescription-level data, we analyzed co-variates including pre-/post-OAP-PM, patient age (continuous), site (academic vs. network), communication with someone other than the primary prescriber (y/n), and medication category (anti-neoplastic vs. supportive). We used control charts to explore the contribution of specific medications and sites that might share best practices or be the focus of subsequent, more focused improvement projects. Results: From May 1, 2022 to May 1, 2023, 458 prescription modifications were completed, 180 pre- and 278 post-OAP-PM. The proportion of prescriptions requiring > 60 minutes to complete decreased from 29.4% to 19.8% (p = 0.0184). In multivariable regression, the significant predictors of prescription taking > 60 minutes to complete were pre-OAP-PM (LR 4.9, p = 0.027); network (LR 20.8, p < 0.0001); and communication with someone other than the primary provider (LR 18.6, p < 0.0010). Control charts demonstrated all sites were within 3-sigma limits pre- and post-OAP-PM, but with wide limits at many sites. Individual medications could not be meaningfully analyzed due to the small sample sizes. Conclusions: OAP-PM successfully reduced the proportion of OA prescriptions taking more than an hour to modify. This may be a useful workflow change for other specialty pharmacies to adopt to improve the efficiency of OA care. Additional work is needed to understand whether site-, provider- or medication-specific changes can improve upon this model.
Heterogeneity in costs of standard-of-care drug regimens within high volume Enhancing Oncology Model disease groups: Implications for performance risk outside of a provider’s control.
Authors: Baker Katherine , Young Garrett , Bilbrey Larry Edward , Mullangi Samyukta , Lyss Aaron J. , Dickson Natalie R. , Owens Leah , Schleicher Stephen Matthew 
Published: October 2023

70 Background: The Enhancing Oncology Model (EOM) is a voluntary two-sided risk model that compares practice expenditures relative to calculated benchmarks for seven cancer types. To account for changes in cancer costs based on clinical characteristics, the EOM applies metastatic (M0 vs. M1) and HER2 clinical adjustments. These adjustments modify lung and small intestine/colorectal episodes for ever-metastatic status by a factor of 1.06 and 1.10, respectively, and breast cancer episodes by a factor of 1.23 for ever-metastatic status and HER2 positivity. However, it is unclear how well these methodological additions capture indication-specific treatment costs of care within an EOM disease group. Methods: Medicare Part B Drug and Biological Average Sales Price (ASP) data were analyzed to estimate total cost to Medicare for a six-month course of systemic therapy within three common EOM disease groups: breast cancer, lung cancer, and colorectal cancer. Diseases were further broken down by M0 vs. M1 and HER2 status consistent with EOM methodology. All treatment regimens selected for analysis are concordant with National Comprehensive Cancer Network guidelines and considered standard of care based on clinical phenotypes – including PD-L1 status, lymph node status, and tumor size – which are not captured by EOM methodology. Results: Within common EOM disease groups and after accounting for EOM clinical adjusters, six-month episode costs of standard of care systemic treatments vary widely based on clinical features not captured in the model. For instance, the cost of care from drug treatment alone varies by over 220-fold for M0 NSCLC, 72-fold for M0 HER2- high risk breast cancer, 7-fold for M0 HER2+ high risk breast cancer, 270-fold for M1 HER2- high risk breast cancer, and 6-fold for M1 colorectal cancer based on clinical features that guide treatment decisions in real practice. Conclusions: Despite methodological attempts to reduce the influence of case mix on performance in EOM, the variability in cost of indication-specific treatments based on clinical characteristics not captured in the model might create performance risk for participating practices. [Table: see text]
Does financial advocacy improve appointment keeping in patients receiving chemotherapy?
Authors: Doherty Meredith , Bresz Kellie , Guglielmo Kimberly , Davella Christopher , Cadet Tamara , Walker Suzanne , Wileyto Paul 
Published: October 2023

75 Background: Financial hardship leads to treatment nonadherence including missed medical appointments. This study describes a financial advocacy intervention, developed in response to Medicare’s Oncology Care Model’s price estimate requirement, and measures its effect on patient-initiated missed appointments (no-shows/cancellations). Methods: Logistic regression was used to analyze the clinical records of 2523 chemotherapy patients, before and after program implementation, at one semi-rural cancer center (2015 – 2018). Cluster-correlated covariance was used to correct variances for repeated measures. Results: Patients were assigned an advocate who provided out-of-pocket price estimates, health insurance education, and followed-up with the patient every 30 days for 6 months. 1,271 patients (50.4%) received the intervention. Average age was 64.1 and the average number of missed appointments was 8.7. After accounting for age, gender, marital status, insurance type, and number of chemotherapy infusions, receiving financial advocacy was associated with a 9% reduction in the odds of a missed appointment (OR = 0.91; 95% CI= 0.84 – 0.98; p = 0.013). Medicaid (OR = 1.36; 95% CI = 1.17 – 1.58; p = 0.000) and divorce (OR = 1.19; 95% CI = 1.03 – 1.37; p = 0.015) increased the odds of missed appointments, older age (OR = 0.99; 95% CI = 0.99 – 1.00, p = 0.000) and male gender (OR = 0.90; 95%CI = 0.84 – 0.98; p = 0.011) reduced the odds of missed appointments. Conclusions: The intervention provided some protection against missed appointments. However, marital status, age, gender, and insurance type remained significantly associated with missed appointments, even after accounting for financial advocacy. These findings indicate the need for more research on financial advocacy to identify the effective core components, target at-risk populations, and understand the effect of implementation on intervention effectiveness.[Table: see text]
Disparities in clinical trial participation beyond first-line treatment among patients with metastatic breast or colorectal cancer.
Authors: Wang Jennifer S. , Johnson Benny , Barcenas Carlos Hernando , Betts Andrea , Murphy Caitlin C. 
Published: October 2023

97 Background: Disparities in clinical trial participation beyond first-line (1L) treatment of metastatic cancer have not been well studied. Using real-world data from patients diagnosed with metastatic breast cancer (mBC) or metastatic colorectal cancer (mCRC), this study evaluated 1) patterns of participation in trials by treatment line and 2) factors associated with participation in trials of second-line (2L) and subsequent lines of treatment. Methods: Patients diagnosed with mBC or mCRC between 2013 and 2021 who received at least 2L treatment were retrospectively identified from the Flatiron Health electronic health record database. Clinical trial participation was defined as receipt of “clinical study drug.” Participation in trials for treatment lines 2L to 6L were assessed, and multivariable logistic regression models were used to evaluate associations between sociodemographic and clinical factors, including age, race/ethnicity, socioeconomic status (SES), stage at diagnosis, year of metastatic diagnosis, practice type, and total lines of treatment, with participation in trials of 2L and subsequent lines of treatment. Results: Overall, participation in trials was low for patients with mBC or mCRC. For patients with mBC (n=14,590), participation in trials was similar from 2L (2.6%) to 6L (2.7%) (Table 1). Among these patients, age, race/ethnicity, and practice type were associated with participation in trials, while SES was not. For example, women age 18-49, 50-64, and 65-74 years were more likely to participate than women 75 or older (aORs: 2.88, 2.45, 1.84), and non-Hispanic Asian, non-Hispanic Black, and Hispanic women were less likely to participate than non-Hispanic white women (aORs: 0.41, 0.47, 0.61). In contrast to patients with mBC, participation in trials increased from 2.0% (2L) to 6.6% (6L) for patients with mCRC (n=7,303; Table 1). Among these patients, age, practice type, and SES were associated with participation in trials. Specifically, patients treated in academic compared to community settings (aOR: 4.25, 95% CI: 3.33, 5.42) and patients with higher SES (aOR: 2.00, 95% CI: 1.29, 3.08) were more likely to participate. For both patients with mBC (aOR: 5.00, 95% CI, 3.87, 6.46) or mCRC (aOR: 7.91, 95% CI: 5.63, 11.10), those who received more than five treatment lines compared to only two treatment lines were more likely to participate in a trial. Conclusions: These real-world findings highlight disparities in participation in clinical trials of 2L and subsequent lines of treatment by age, race/ethnicity, practice type, and SES among patients with mBC or mCRC. Further policy efforts and interventions are needed to address systematic barriers to increase participation of underrepresented groups in clinical trials.[Table: see text]
The Prognosis of Glutamic Acid Decarboxylase Antibodies in Women With Hyperglycemia in Pregnancy
Authors: Cosson Emmanuel , Thioye Elhadji Mamadou Moussa , Tatulashvili Sopio , Vicaut Eric , Larger Etienne , Sal Meriem , Pinto Sara , Fabre Emmanuelle , Lalatonne Yoann , Sutton Angela , Nachtergaele Charlotte , Portal Jean-Jacques , Zerguine Mohamed , Carbillon Lionel , Bihan Hélène 
Published: October 2023

Abstract

Context
We recently reported that the presence of glutamic acid decarboxylase antibodies (GADA) was not associated with large-for-gestational-age infants in women with hyperglycemia in pregnancy (HIP).

Objective
We explored the association between the presence of GADA and other HIP-related adverse pregnancy outcomes.

Methods
This observational prospective study, conducted at a university hospital in a suburb of Paris, France, included 1182 consecutive women with HIP measured for GADA at HIP care initiation between 2012 and 2017. Post hoc analyses for outcomes included gestational weight gain, insulin therapy, cesarean delivery, hypertensive disorders, small-for-gestational-age infant, prematurity, and neonatal hypoglycemia.

Results
Of the 1182 women studied, 87 (7.4%) had positive (≥ 1 IU/mL) GADA. Although socioeconomic, clinical, and biological characteristics were similar across women in the positive and negative GADA groups, higher fasting plasma glucose values during early HIP screening were observed in the former (5.5 ± 1.5 vs 5.2 ± 0.7 mmol/L respectively, P &lt; .001). At HIP care initiation, fructosamine levels were higher in women with positive GADA (208 ± 23 vs 200 ± 18 µmol/L; P &lt; .05). In the homeostatic model assessment, insulin resistance (HOMA-IR) and beta secretion (HOMA-B) rates were similar in both groups. Gestational weight gain and the rates of all adverse outcomes were similar in both groups except for cesarean delivery (18.4 and 27.3% for positive and negative GADA, respectively; adjusted odds ratio 0.49 [95% CI, 0.26-0.92], P = .026).

Conclusion
Universal measurement of GADA in women with HIP highlighted that 7.4% had positive GADA. No association was observed between GADA and HIP-related adverse pregnancy outcomes, except a lower risk of cesarean delivery.
The Power of the Heterogeneous Stock Rat Founder Strains in Modeling Metabolic Disease
Authors: Wagner Valerie A , Holl Katie L , Clark Karen C , Reho John J , Lehmler Hans-Joachim , Wang Kai , Grobe Justin L , Dwinell Melinda R , Raff Hershel , Kwitek Anne E 
Published: October 2023

Abstract
Metabolic diseases are a host of complex conditions, including obesity, diabetes mellitus, and metabolic syndrome. Endocrine control systems (eg, adrenals, thyroid, gonads) are causally linked to metabolic health outcomes. N/NIH Heterogeneous Stock (HS) rats are a genetically heterogeneous outbred population developed for genetic studies of complex traits. Genetic mapping studies in adult HS rats identified loci associated with cardiometabolic risks, such as glucose intolerance, insulin resistance, and increased body mass index. This study determined underappreciated metabolic health traits and the associated endocrine glands within available substrains of the HS rat founders. We hypothesize that the genetic diversity of the HS rat founder strains causes a range of endocrine health conditions contributing to the diversity of cardiometabolic disease risks. ACI/EurMcwi, BN/NHsdMcwi, BUF/MnaMcwi, F344/StmMcwi, M520/NRrrcMcwi, and WKY/NCrl rats of both sexes were studied from birth until 13 weeks of age. Birth weight was recorded, body weight was measured weekly, metabolic characteristics were assessed, and blood and tissues were collected. Our data show wide variation in endocrine traits and metabolic health states in ACI, BN, BUF, F344, M520, and WKY rat strains. This is the first report to compare birth weight, resting metabolic rate, endocrine gland weight, hypothalamic–pituitary–thyroid axis hormones, and brown adipose tissue weight in these rat strains. Importantly, this work unveils new potential for the HS rat population to model early life adversity and adrenal and thyroid pathophysiology. The HS population likely inherited risk alleles for these strain-specific traits, making the HS rat a powerful model to investigate interventions on endocrine and metabolic health.