bluuebunny/crossref_metadata_2025_split huggingface dataset:
CT enterography for evaluation of disease activity in patients with ileocolonic Crohn's disease
Authors: Tong Jinlu , Feng Qi , Zhang Chenpeng , Xu Xitao , Ran Zhihua 
Published: June 2022

Abstract
Background
CT enterography (CTE) is used routinely for assessment of activity and severity in Crohn's disease (CD), but there are few CTE scoring systems. The aim of this study was to develop a quantitative CTE scoring system for ileocolonic Crohn's disease activity.

Methods
Forty-nine CD patients with ileocolonic involvement were retrospectively included between March 2015 and May 2018. All patients underwent CTE and ileocolonoscopy. Mural hyperenhancement and mural thickening at CTE were scored quantitatively, while mural stratification, submucosal fat deposition, comb sign, perienteric fat hypertrophy and mesenteric fibrofatty proliferation were qualitative variables. A Tobit regression model was applied for assessing the association between Crohn's disease endoscopic index of severity (CDEIS) and CTE variables.

Results
A total of 280 intestinal segments were evaluated. Independent predictors for CDEIS were mural thickness (p < 0.001), mural stratification (p < 0.001) and comb sign (p = 0.002). In order to quantify disease activity based on CTE findings in each segment, a simplified CT enterography index of activity (CTEIA) was derived from logistic regression analysis. The formula was as follows: CTEIA (segment) = 2.1 mural thickness(mm) + 9.7 mural stratification + 5.2 comb sign. There was a high and significant correlation coefficient between CDEIS and CTEIA (r = 0.779, p < 0.001) for per-segment analysis. The model for the detection of ulcerative lesions in the colon and terminal ileum achieved an area under the receiver-operating curve of 0.901 using a cut-off point of 6.25.

Conclusions
CTEIA is a new qualitative tool for evaluation of ileocolonic Crohn’s disease, which need to be validated in further studies.
Risk of recurrent stillbirth and neonatal mortality: mother-specific random effects analysis using longitudinal panel data from Indonesia (2000 – 2014)
Authors: Dev Alka 
Published: June 2022

Abstract
Background
Despite significant government investments to improve birth outcomes in low and middle-income countries over the past several decades, stillbirth and neonatal mortality continue to be persistent public health problems. While they are different outcomes, there is little evidence regarding their shared and unique population-level risk factors over a mother’s reproductive lifespan. Data gaps and measurement challenges have left several areas in this field unexplored, especially assessing the risk of stillbirth or neonatal mortality over successive pregnancies to the same woman. This study aimed to assess the risk of stillbirth and neonatal mortality in Indonesia during 2000–2014, using maternal birth histories from the Indonesia Family Life Survey panel data.

Methods
Data from three panels were combined to create right-censored birth histories. There were 5,002 unique multiparous mothers with at least two singleton births in the sample. They reported 12,761 total births and 12,507 live births. Random effects (RE) models, which address the dependency of variance in births to the same mother, were fitted assuming births to the same mother shared unobserved risk factors unique to the mother.

Results
The main finding was that there having had a stillbirth increased the odds of another stillbirth nearly seven-fold and that of subsequent neonatal mortality by over two-fold. Having had a neonatal death was not associated with a future neonatal death. Mothers who were not educated and nullipara were much more likely to experience a neonatal death while mothers who had a prior neonatal death had no risk of another neonatal death due to unmeasured factors unique to the mother.

Conclusions
The results suggest that for stillbirths, maternal heterogeneity, as explained by a prior stillbirth, could capture underlying pathology while the relationship between observed risk factors and neonatal mortality could be much more dependent on context. Establishing previous adverse outcomes such as neonatal deaths and stillbirth could help identify high-risk pregnancies during prenatal care, inform interventions, and improve health policy.
Characteristic computed tomography features in mesenchymal-epithelial transition exon14 skipping-positive non-small cell lung cancer
Authors: Watari Naokazu , Yamaguchi Kakuhiro , Terada Hiroaki , Hamai Kosuke , Masuda Ken , Nishimura Yoshifumi , Sakamoto Shinjiro , Masuda Takeshi , Horimasu Yasushi , Miyamoto Shintaro , Nakashima Taku , Iwamoto Hiroshi , Shoda Hiroyasu , Ishikawa Nobuhisa , Fujitaka Kazunori , Miyazaki Kozue , Miyata Yoshihiro , Hamada Hironobu , Awai Kazuo , Hattori Noboru 
Published: June 2022

Abstract
Background
Mesenchymal-epithelial transition exon14 (METex14) skipping is one of the therapeutic driver oncogene mutations in non-small cell lung cancer (NSCLC), and can be treated with tepotinib and capmatinib. There is only one report on computed tomography (CT) findings of METex14 skipping-positive NSCLC, which shows that the primary tumor tends to have a large mass in the upper lobe, and extrathoracic metastases are common. This study examined the CT findings of METex14 skipping-positive NSCLC, focusing on the features of the margins and internal structures.

Methods
We consecutively included patients with METex14 skipping-positive NSCLC who were diagnosed between January 2018 and December 2020 at four independent institutions. We retrospectively reviewed the patient demographics and CT findings for tumor margins (invasion into surrounding tissue, lobulation, pleural indentation, spicula, and ground-glass opacity) and internal structures (air bronchograms, cavitation and internal low-density area).

Results
Fifteen patients with METex14 skipping-positive NSCLC were identified. Almost half of the patients were men (7/15; 46.7%), and their median age was 75.0 years. More than half were either current or former smokers (9/15; 60.0%). A vast majority of histological subtypes were adenocarcinoma (10/15; 66.7%), followed by pleomorphic carcinoma (3/15; 20.0%) and squamous cell carcinoma (2/15; 13.3%). With regard to CT findings, most primary tumors presented as masses larger than 30 mm (12/15; 80.0%) and were located in the upper lobes (12/15; 80.0%). Invasion into surrounding tissue and presence of internal low-density areas were observed in 60.0% (9/15) and 66.7% (10/15) of the primary tumors, respectively. Additionally, their frequencies increased to 72.7% (8/11) and 90.9% (10/11) in stage III/IV cases, respectively. In lymph node metastasis, internal low-density areas were observed in 8/10 cases (80.0%). Although these two CT features were rarely observed in distant metastases at diagnosis, they became apparent with progression of the metastatic tumor size.

Conclusions
METex14 skipping-positive NSCLC tumors tend to invade surrounding tissue and possess internal low-density areas. These CT findings might be characteristic of METex14 skipping-positive NSCLC.
Clinical predictors for nondiabetic kidney diseases in patients with type 2 diabetes mellitus: a retrospective study from 2017 to 2021
Authors: Zeng Yong-qin , Yang Yu-xing , Guan Cheng-jing , Guo Zi-wei , Li Bo , Yu Hai-yan , Chen Rui-xue , Tang Ying-qian , Yan Rui 
Published: June 2022

Abstract
Background
Nondiabetic kidney disease (NDKD), which is prevalent among patients with diabetes mellitus (DM), is considerably different from diabetic kidney disease (DKD) in terms of the pathological features, treatment strategy and prognosis. Although renal biopsy is the current gold-standard diagnostic method, it cannot be routinely performed due to a range of risks. The aim of this study was to explore the predictors for differentiating NDKD from DKD to meet the urgent medical needs of patients who cannot afford kidney biopsy.

Methods
This is a retrospective study conducted by reviewing the medical records of patients with type 2 DM who underwent percutaneous renal biopsy at the Affiliated Hospital of Guizhou Medical University between January 2017 and May 2021. The demographic data, clinical data, blood test results, and pathological examination results of the patients were obtained from their medical records. Multivariate regression analysis was performed to evaluate the predictive factors for NDKD.

Results
A total of 244 patients were analyzed. The median age at biopsy was 55 (46, 62) years. Patients diagnosed with true DKD, those diagnosed with NDKD and those diagnosed with NDKD superimposed DKD represented 48.36% (118/244), 45.9% (112/244) and 5.74% (14/244), respectively, of the patient population. Immunoglobulin A nephropathy was the most common type of lesion in those with NDKD (59, 52.68%) and NDKD superimposed DKD (10, 71.43%). Independent predictive indicators for diagnosing NDKD included a DM duration of less than 5 years (odds ratio [OR] = 4.476; 95% confidence interval [CI]: 2.257–8.877; P < 0.001), an absence of diabetic retinopathy (OR = 4.174; 95% CI: 2.049–8.502; P < 0.001), a high RBC count (OR = 1.901; 95% CI: 1.251–2.889; P = 0.003), and a negative of urinary glucose excretion test result (OR = 2.985; 95% CI: 1.474–6.044; P = 0.002)..

Conclusions
A DM duration less than 5 years, an absence of retinopathy, a high RBC count and an absence of urinary glucose excretion were independent indicators for the diagnosis of NDKD, suggesting that patients with NDKD may require a different treatment regimen than those with DKD.
Decision aids linked to the recommendations in clinical practice guidelines: results of the acceptability of a decision aid for patients with generalized anxiety disorder
Authors: Ramos-García Vanesa , Perestelo-Pérez Lilisbeth , Rivero-Santana Amado , Peñate-Castro Wenceslao , Duarte-Díaz Andrea , Álvarez-Pérez Yolanda , Trujillo-Martín María del Mar , del Cura-González María Isabel , Serrano-Aguilar Pedro 
Published: June 2022

AbstractBackgroundGeneralized anxiety disorder (GAD) is one of the most prevalent mental health problems. Patients with GAD have unmet needs related to the information received about their disorder, its treatments and their participation in the decision-making process. The aim of this study is to develop and assess the acceptability of a patient decision aid (PtDA) for patients with GAD.MethodThe PtDA was developed following the International Patient Decision Aid Standards. The recommendations of the Spanish clinical practice guideline (CPG) for patients with GAD were used as the basis. The first prototype was developed by an expert committee, further improvements were made with patients (n = 2), clinical experts (n = 13) and the project management group (n = 7). The acceptability of this second draft was assessed by patients non-involved in the previous phases (n = 11).ResultsThe final PtDA version included a brief description of GAD and its treatments. Most participants agreed that the PtDA was easy to use, visually appealing and useful. At least half of the participants learned new things about treatments and adverse effects.ConclusionsA PtDA was developed for patients with GAD based on recommendations from the Spanish CPG. It was improved and accepted by patients and clinical experts involved. An evaluation of its effectiveness on the shared decision-making process during the clinical encounter is planned.
Validation of a computerized decision support system to review pharmacotherapy treatment: scheduling guidelines
Authors: Zapelini Clávison Martinelli , Galato Dayani , Alano Graziela Modolon , de Carvalho Martins Karina Saviatto , Trauthman Silvana Cristina , Soares Alessandra , Schuelter-Trevisol Fabiana , Trevisol Daisson José 
Published: June 2022

AbstractBackgroundThe review of pharmacotherapy can be conceptualized as a service in which the drugs used by the patient are reviewed to control the risks as well as to improve the results of the drug therapy, detecting, solving, and preventing issues associated with the drug, readjusting the doses and times (schedule) so that the treatment is not incompatible or in duplicity.MethodsThe aim of the study was to validate an intelligent information system, which was developed to assist the scheduling activity in the pharmacotherapy review. The system used the concept of Genetic Algorithms. To validate the system, hypothetical cases were elaborated considering various aspects of pharmacotherapy such as underdose, overdose, drug interactions and contraindications. These cases were tested in the system and were also analyzed by pharmaceutical experts with clinical and research experience in the pharmacotherapy review process. The degree of agreement between the assessments of the appointments carried out by the pharmaceutical specialists and by the system were measured using the Kappa index with a 95% confidence interval.ResultsIn detecting errors and make propositions, the system was able to identify 80% of errors, with pharmaceutical experts identifying between 20 and 70% of errors. In relation the results of kappa between the cases, the system had 87,3% of concordance, whereas the best pharmaceutical expert had 75,5% of concordance, considering the correct answer.ConclusionIt can be concluded that with the methodology used, the investigation met the objectives and confirmed the system is effective for pharmaceutical review process. There are indications that the system can help in the Pharmacotherapy review process, being able to find prescription errors as well as to establish times for the use of medications according to the patient’s routine.
Perceptions and patient care needs among hepatitis B patients during COVID-19
Authors: Wallington Sherrie Flynt , Jeon Min Jeong , Nguyen T. Angeline , Byambaa Choosonjargal , Yang Y. Tony , Le Daisy 
Published: June 2022

Abstract
Background
The novel coronavirus of 2019 (COVID-19) has been and continues to be a rapidly developing public health crisis, that has also disrupted routine and maintenance health care for people living with chronic conditions. Some of these chronic conditions also put individuals at increased risk of COVID-19 complications, particularly if the condition is not under control. For these reasons, the exploratory study reported here examined the needs and preparedness of patients at a community health organization that specifically provides hepatitis B virus (HBV) care for high-risk groups that had previously tested positive for HBV.

Methods
Current study utilized exploratory analysis of qualitative COVID-19-related statements collected during calls to a total of 44 patients reached during April and May, 2020 in the Washington D.C. area. Researchers worked with a community based non-profit organization to reach current HBV + and HCV + patients to provide retention in care and assess patient needs in maintaining management of their condition adapted to include offering medication refills, telehealth, and other resources. We gathered emergent themes, using socio-ecological framework, regarding capacity and needs for managing their chronic condition in a vulnerable population during the initial, most interrupted, time period of a global public health crisis.

Results
From the notes of the calls, five thematic categories emerged: COVID-19 prevention awareness, assistance program access, medical resource access, access to knowledge and awareness about assistance programs, and needs and barriers. From these five themes, providers can develop strategies to better prepare their patients and provide care to patients with chronic conditions during major disruptions.

Conclusions
Future recommendations include increasing hepatitis and COVID-19 vaccine efforts, collaborating with community partners, and screening and understanding social determinants of health that affect racial and ethnic minorities.
Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?
Authors: Ricket Iben M. , MacKenzie Todd A. , Emond Jennifer A. , Ailawadi Kusum L. , Brown Jeremiah R. 
Published: June 2022

Abstract
Background
Super-utilizers represent approximately 5% of the population in the United States (U.S.) and yet they are responsible for over 50% of healthcare expenditures. Using characteristics of hospital service areas (HSAs) to predict utilization of resource intensive healthcare (RIHC) may offer a novel and actionable tool for identifying super-utilizer segments in the population. Consumer expenditures may offer additional value in predicting RIHC beyond typical population characteristics alone.

Methods
Cross-sectional data from 2017 was extracted from 5 unique sources. The outcome was RIHC and included emergency room (ER) visits, inpatient days, and hospital expenditures, all expressed as log per capita. Candidate predictors from 4 broad groups were used, including demographics, adults and child health characteristics, community characteristics, and consumer expenditures. Candidate predictors were expressed as per capita or per capita percent and were aggregated from zip-codes to HSAs using weighed means. Machine learning approaches (Random Forrest, LASSO) selected important features from nearly 1,000 available candidate predictors and used them to generate 4 distinct models, including non-regularized and LASSO regression, random forest, and gradient boosting. Candidate predictors from the best performing models, for each outcome, were used as independent variables in multiple linear regression models. Relative contribution of variables from each candidate predictor group to regression model fit were calculated.

Results
The median ER visits per capita was 0.482 [IQR:0.351–0.646], the median inpatient days per capita was 0.395 [IQR:0.214–0.806], and the median hospital expenditures per capita was $2,302 [1$,544.70-$3,469.80]. Using 1,106 variables, the test-set coefficient of determination (R2) from the best performing models ranged between 0.184–0.782. The adjusted R2 values from multiple linear regression models ranged from 0.311–0.8293. Relative contribution of consumer expenditures to model fit ranged from 23.4–33.6%.

Discussion
Machine learning models predicted RIHC among HSAs using diverse population data, including novel consumer expenditures and provides an innovative tool to predict population-based healthcare utilization and expenditures. Geographic variation in utilization and spending were identified.
Identifying drivers of health care value: a scoping review of the literature
Authors: Landon Susan N. , Padikkala Jane , Horwitz Leora I. 
Published: June 2022

Abstract
Background
As health care spending reaches unsustainable levels, improving value has become an increasingly important policy priority. Relatively little research has explored factors driving value. As a first step towards filling this gap, we performed a scoping review of the literature to identify potential drivers of health care value.

Methods
Searches of PubMed, Embase, Google Scholar, Policy File, and SCOPUS were conducted between February and March 2020. Empirical studies that explored associations between any range of factors and value (loosely defined as quality or outcomes relative to cost) were eligible for inclusion. We created a template in Microsoft Excel for data extraction and evaluated the quality of included articles using the Critical Appraisal Skills Programme (CASP) quality appraisal tool. Data was synthesized using narrative methods.

Results
Twenty-two studies were included in analyses, of which 20 focused on low value service utilization. Independent variables represented a range of system-, hospital-, provider-, and patient-level characteristics. Although results were mixed, several consistent findings emerged. First, insurance incentive structures may affect value. For example, patients in Accountable Care Organizations had reduced rates of low value care utilization compared to patients in traditionally structured insurance plans. Second, higher intensity of care was associated with higher rates of low value care. Third, culture is likely to contribute to value. This was suggested by findings that recent medical school graduation and allopathic training were associated with reduced low value service utilization and that provider organizations had larger effects on value than did individual physicians.

Conclusions
System, hospital, provider, and community characteristics influence low value care provision. To improve health care value, strategies aiming to reduce utilization of low value services and promote high value care across various levels will be essential.
Surgical treatment on infective endocarditis: impact of diabetes on mortality
Authors: Kogan Alexander , Wieder-Finesod Anat , Frogel Jonathan , Peled-Potashnik Yael , Ram Eilon , Raanani Ehud , Sternik Leonid 
Published: June 2022

Abstract
Background
Type 2 diabetes mellitus (DM) is a frequent co-morbidity among patients suffering from infective endocarditis (IE). The aim of the study was to evaluate the impact of type 2 DM on the early-, intermediate- and long-term mortality of patients who underwent surgical treatment of endocarditis.

Methods
We performed an observational cohort study in the large tertiary center in Israel during 14 years. All data of patients who underwent surgical treatment of endocarditis, performed between 2006 and 2020 were extracted from the departmental database. Patients were divided into two groups: Group I (non-diabetic patients), and Group II (diabetic patients).

Results
The study population includes 420 patients. Group I (non-diabetic patients), comprise 326 patients, and Group II (diabetic patients), comprise 94 patients. Mean follow-up duration was 39.3 ± 28.1 months. Short-term, 30-day and in-hospital mortality, also intermediate-term mortality (1- and 3-year) was higher in the DM group compared with the non-DM group, but did not reach statistical significance: 11.7% vs. 7.7%. (p = 0.215); 12.8% vs. 8.3% (p = 0.285); 20.2% vs. 13.2% (p = 0.1) and 23.4% vs. 15.6% (p = 0.09) respectively. Long-term, 5-year mortality was significantly higher in the DM group, compared to the non-DM group: 30.9% vs. 16.6% (p = 0.003). Furthermore, predictors for long-term mortality included diabetes (CI 1.056–2.785, p = 0.029), as demonstrated by regression analysis.

Conclusions
Diabetic patients have trend to increasing mortality at the short- and intermediate period post-surgery for IE, but this is not statistically significant. Survival of diabetic patients deteriorates after more than three years follow surgery. Diabetes is an independent predictor for long-term, 5-year mortality after surgical treatment of endocarditis, regardless of the patients age and comorbidities.
Trial registration Ethical Committee of Sheba Medical Centre, Israel on 02.12. 2014, Protocol 4257