bluuebunny/crossref_metadata_2025_split huggingface dataset:
Derivation and validation of a nomogram for predicting nonventilator hospital-acquired pneumonia among older hospitalized patients
Authors: Chen Zhihui , Xu Ziqin , Wu Hongmei , Gao Shengchun , Wang Haihong , Jiang Jiaru , Li Xiuyang , Chen Le 
Published: April 2022

Abstract
Background
Currently, there is no effective tool for predicting the risk of nonventilator hospital-acquired pneumonia (NV-HAP) in older hospitalized patients. The current study aimed to develop and validate a simple nomogram and a dynamic web-based calculator for predicting the risk of NV-HAP among older hospitalized patients.

Methods
A retrospective evaluation was conducted on 15,420 consecutive older hospitalized patients admitted to a tertiary hospital in China between September 2017 and June 2020. The patients were randomly divided into training (n = 10,796) and validation (n = 4624) cohorts at a ratio of 7:3. Predictors of NV-HAP were screened using the least absolute shrinkage and selection operator method and multivariate logistic regression. The identified predictors were integrated to construct a nomogram using R software. Furthermore, the optimum cut-off value for the clinical application of the model was calculated using the Youden index. The concordance index (C-index), GiViTI calibration belts, and decision curve were analysed to validate the discrimination, calibration, and clinical utility of the model, respectively. Finally, a dynamic web-based calculator was developed to facilitate utilization of the nomogram.

Results
Predictors included in the nomogram were the Charlson comorbidity index, NRS-2002, enteral tube feeding, Barthel Index, use of sedatives, use of NSAIDs, use of inhaled steroids, and "time at risk". The C-index of the nomogram for the training and validation cohorts was 0.813 and 0.821, respectively. The 95% CI region of the GiViTI calibration belt in the training (P = 0.694) and validation (P = 0.614) cohorts did not cross the diagonal bisector line, suggesting that the prediction model had good discrimination and calibration. Furthermore, the optimal cut-off values for the training and validation cohorts were 1.58 and 1.74%, respectively. Analysis of the decision curve showed that the nomogram had good clinical value when the threshold likelihood was between 0 and 49%.

Conclusion
The developed nomogram can be used to predict the risk of NV-HAP among older hospitalized patients. It can, therefore, help healthcare providers initiate targeted medical interventions in a timely manner for high-risk groups.
Association of lumbar disc degeneration with low back pain in middle age in the Northern Finland Birth Cohort 1966
Authors: Mertimo Teija , Karppinen Jaro , Niinimäki Jaakko , Blanco Roberto , Määttä Juhani , Kankaanpää Markku , Oura Petteri 
Published: April 2022

Abstract
Background
Although it has been suggested that lumbar disc degeneration (LDD) is a significant risk factor for low back pain (LBP), its role remains uncertain. Our objective was to clarify the association between LDD and LBP and whether mental distress modifies the association.

Methods
Participants of a birth cohort underwent 1.5-T lumbar magnetic resonance imaging at the age of 47. The association between the sum score of LDD (Pfirrmann classification, range 0–15) and LBP (categorized into “no pain”, “mild-to-moderate pain”, “bothersome-and-frequent pain”) was assessed using logistic regression analysis, with sex, smoking, body mass index, physical activity, occupational exposure, education, and presence of Modic changes and disc herniations as confounders. The modifying role of mental distress (according to the Hopkins Symptom Check List-25 [HSCL-25], the Beck Depression Inventory and the Generalized Anxiety Disorder Scale) in the association was analyzed using linear regression.

Results
Of the study population (n = 1505), 15.2% had bothersome and frequent LBP, and 29.0% had no LBP. A higher LDD sum score increased the odds of belonging to the “mild-to-moderate pain” category (adjusted OR corresponding to an increase of one point in the LDD sum score 1.11, 95% CI 1.04–1.18, P = 0.003) and the “bothersome-and-frequent pain” category (adjusted OR 1.20, 95% CI 1.10–1.31, P < 0.001), relative to the “no pain” category. Mental distress significantly modified the association between LDD and LBP, as a linear positive association was consistently observed among individuals without mental distress according to HSCL-25 (adjusted B 0.16, 95% CI 0.07–0.26, P < 0.001), but not among individuals with higher mental distress.

Conclusions
LDD was significantly associated with both mild-to-moderate and bothersome-and-frequent LBP. However, the co-occurrence of mental distress diminished the association between LDD and LBP bothersomeness. Our results strongly suggest that mental symptoms affect the pain experience.
Value of the triglyceride glucose index combined with body mass index in identifying non-alcoholic fatty liver disease in patients with type 2 diabetes
Authors: Li Nong , Tan Huiwen , Xie Aixia , Li Cheng , Fu Xuan , Xang Weiting , Kirim Amina , Huang Xuefang 
Published: April 2022

Abstract
Background
The triglyceride glucose index combined with body mass index is a new index that reflects the degree of insulin resistance. In this cross-sectional study, we aimed to explore the predictive value of the triglyceride glucose-body mass index (TyG-BMI) in relation to the occurrence of non-alcoholic fatty liver disease (NAFLD) in the Chinese population with type 2 diabetes (T2D).

Methods
We selected 826 patients with T2D who were hospitalized at the Department of Endocrinology and Metabolism of Karamay People’s Hospital from September 2016 to October 2018 for this research. The height, weight, fasting blood glucose, serum insulin, and lipid profiles of the subjects were collected. The liver ultrasound showed any degree of echogenic enhancement of liver tissue and the liver appeared brighter than the renal cortex on ultrasound were considered to be NAFLD. The logistic regression analysis was performed to estimate associations between the triglyceride glucose index (TyG), TyG-BMI index, insulin resistance index (HOMA-IR) and the ratio of the triglycerides to high-density lipoprotein-cholesterol with a diagnosis of NAFLD. The receiver operating characteristic curve method was used to analyze its predictive value for NAFLD.

Results
Results of the logistic regression analysis showed that the odds ratios of NAFLD were 6.535 (3.70–11.53) and 4.868 (2.576–9.200) for the TyG-BMI before and after correction,respectively(P < 0.001). The area under the curve (AUC) for TyG-BMI was 0.727 (0.691–0.764), which was the highest among all the other parameters studied.

Conclusion
Compared with the TyG index, the TG/HDL-C and HOMA-IR, the TyG-BMI was a more effective predictor of NAFLD in T2D.
Primary health care case management through the lens of complexity: an exploratory study of naturopathic practice using complexity science principles
Authors: Graham Kim D. , Steel Amie , Wardle Jon 
Published: April 2022

AbstractBackgroundAdvances in systems science creates an opportunity to bring a complexity perspective to health care practices and research. While medical knowledge has greatly progressed using a reductionist and mechanistic philosophy, this approach may be limited in its capacity to manage chronic and complex illness. With its holistic foundation, naturopathy is a primary health profession with a purported alignment with a complexity perspective. As such this pilot study aimed to investigate the application of complexity science principles, strategies, and tools to primary health care using naturopathy as a case study.MethodsA network mapping and analysis of the naturopathic case management process was conducted. Mind maps were created by naturopathic practitioners to reflect their clinical conceptualisation of a common paper clinical case. These mind maps were inputed intoGephi, a network mapping, exploration, and analysis software. Various layouts of the data were produced, and these were analysed using exploratory data analysis and computational network analysis.ResultsSeven naturopathic practitioners participated in the study. In the combined network mapping, 133 unique elements and 399 links were identified. Obesity, the presenting issue in the case, was centrally located. Along with obesity, other keystone elements included: systemic inflammation, dysbiosis, diet, the liver, and mood. Each element was connected on average to 3.05 other elements, with a degree variation between one and 36. Six communities within the dataset were identified, comprising: the nervous system and mood, gastroinstetinal and dietary factors, systemic inflammation and obesity, the endocrine system and metabolism.ConclusionsThis pilot study demonstrates that it is feasible to apply a complexity science perspective to investigating primary health care case management. This supports a shift to viewing the human organism as a complex adaptive system within primary health care settings, with implications for health care practices that are more cognisant with the treatment of chronic and complex conditions and research opportunities to capture the complex clinical reasoning processes of practitioners.
Retrospective descriptive assessment of clinical decision support medication-related alerts in two Saudi Arabian hospitals
Authors: Alsaidan Jamilah Ahmed , Portlock Jane , Ata Sondus I. , Aljadhey Hisham S. , Franklin Bryony Dean 
Published: April 2022

Abstract
Objectives
To determine the frequency of clinical decision support system (CDSS) medication-related alerts generated, accepted, or overridden, to assess appropriateness of alert display and overrides, and to characterise the documentation of clinician justification for these overrides in an academic medical centre in Saudi Arabia.

Materials and methods
System-generated CDSS reports for the period June 2015 to December 2017 were retrospectively reviewed and analysed. Alerts were classified into different types, and rates of alert overrides calculated as percentages of all generated alerts. A subset of 307 overridden alerts was assessed for appropriateness of display and override by two clinical pharmacists. Physician documentation of reasons for overriding alerts were categorised.

Results
A total of 4,446,730 medication-related alerts were generated from both inpatient and outpatient settings, and 4,231,743 (95.2%) were overridden. The most common alert type was ‘duplicate drug’, accounting for 3,549,736 (79.8%) of alerts. Of 307 alerts assessed for appropriateness, 246 (80%) were judged to be appropriately displayed and 244 (79%) were overridden appropriately. New drug allergy and drug allergy alerts had the highest percentage of being judged as inappropriately overridden. For 1,594,313 alerts (37.7%), ‘no overridden reason selected’ was chosen from the drop-down menu.

Conclusions
The alert generation and override rate were higher than reported previously in the literature. The small sample size of 307 alerts assessed for appropriateness of alert display and override is a potential limitation. Revision of the CDSS rules for alerts (focusing on specificity and relevance for the local context) is now recommended. Future research should prospectively assess providers’ perspectives, and determine patient harm associated with overridden alerts.
Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning
Authors: D’Anniballe Vincent M. , Tushar Fakrul Islam , Faryna Khrystyna , Han Songyue , Mazurowski Maciej A. , Rubin Geoffrey D. , Lo Joseph Y. 
Published: April 2022

Abstract
Background
There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation.

Methods
We used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithms beyond pre-defined keywords, attention-guided recurrent neural networks (RNN) were trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for each organ system. Alternative effects on disease classification performance were evaluated using random initialization or pre-trained embedding as well as different sizes of training datasets. The RBA was tested on a subset of 2158 manually labeled reports and performance was reported as accuracy and F-score. The RNN was tested against a test set of 48,758 reports labeled by RBA and performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method.

Results
Manual validation of the RBA confirmed 91–99% accuracy across the 15 different labels. Our models extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random initialization across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with a relatively small number of cases. Pre-trained classification AUCs reached > 0.95 for all four disease outcomes and normality across all three organ systems.

Conclusions
Our label-extracting pipeline was able to encompass a variety of cases and diseases in body CT reports by generalizing beyond strict rules with exceptional accuracy. The method described can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.
l-Arabinose improves hypercholesterolemia via regulating bile acid metabolism in high-fat-high-sucrose diet-fed mice
Authors: Wang Yu , Zhao Jiajia , Li Qiang , Liu Jinxin , Sun Yujie , Zhang Kuiliang , Fan Mingcong , Qian Haifeng , Li Yan , Wang Li 
Published: April 2022

Abstract
Background
Hypercholesterolemia is closely associated with an increased risk of cardiovascular diseases. l-Arabinose exhibited hypocholesterolemia properties, but underlying mechanisms have not been sufficiently investigated. This study aimed to elucidate the mechanisms of l-arabinose on hypocholesterolemia involving the enterohepatic circulation of bile acids.

Methods
Thirty six-week-old male mice were randomly divided into three groups: the control group and the high-fat-high-sucrose diet (HFHSD)-fed group were gavaged with distilled water, and the l-arabinose-treated group were fed HFHSD and received 400 mg/kg/day l-arabinose for 12 weeks. Serum and liver biochemical parameters, serum and fecal bile acid, cholesterol and bile acid metabolism-related gene and protein expressions in the liver and small intestine were analyzed.

Results
l-Arabinose supplementation significantly reduced body weight gain, lowered circulating low-density lipoprotein cholesterol (LDL-C) while increasing high-density lipoprotein cholesterol (HDL-C) levels, and efficiently alleviated hepatic inflammation and lipid accumulations in HFHSD-fed mice. l-Arabinose inhibited cholesterol synthesis via downregulation of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR). Additionally, l-arabinose might facilitate reverse cholesterol transport, evidenced by the increased mRNA expressions of low-density lipoprotein receptor (LDL-R) and scavenger receptor class B type 1 (SR-B1). Furthermore, l-arabinose modulated ileal reabsorption of bile acids mainly through downregulation of ileal bile acid-binding protein (I-BABP) and apical sodium-dependent bile acid transporter (ASBT), resulting in the promotion of hepatic synthesis of bile acids via upregulation of cholesterol-7α-hydroxylase (CYP7A1).

Conclusions
l-Arabinose supplementation exhibits hypocholesterolemic effects in HFHSD-fed mice primarily due to regulation of bile acid metabolism-related pathways.
What is the impact of intellectual property rules on access to medicines? A systematic review
Authors: Tenni Brigitte , Moir Hazel V. J. , Townsend Belinda , Kilic Burcu , Farrell Anne-Maree , Keegel Tessa , Gleeson Deborah 
Published: April 2022

Abstract
Background
It is widely accepted that intellectual property legal requirements such as patents and data exclusivity can affect access to medicines, but to date there has not been a comprehensive review of the empirical evidence on this topic. The World Trade Organization’s Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) requires Member States to implement minimum standards of intellectual property protection including patents for pharmaceutical products, but also contains ‘flexibilities’ designed to address barriers to access to medicines. National intellectual property laws can also include TRIPS-plus rules that go beyond what is required by TRIPS. We aimed to systematically review literature that measures the impact of intellectual property rules on access to medicines, whether implemented as a result of TRIPS, TRIPS-plus provisions in other trade agreements, or unilateral policy decisions.

Methods
We searched Proquest, SCOPUS, Web of Science, PubMed, JSTOR, Westlaw and Lexis Nexis. Peer reviewed articles, government reports and other grey literature were included. Articles were eligible for inclusion if they were quantitative, in English, included a measure of cost, price, availability of or access to medicines, were about intellectual property or data exclusivity rules and published between January 1995 and October 2020. Ninety-one studies met our inclusion criteria. We systematically reviewed the studies’ findings and evaluated their quality using a modified quality assessment template.

Results and conclusion
Five broad overarching themes and 11 subthemes were identified based on the articles’ foci. They were: trade agreements (divided into EU FTAs and those that include the USA); use of TRIPS flexibilities (divided into compulsory licencing and parallel importation); patent expiry/generic entry/generic pathway (divided into comparative studies and single country studies); patent policies (also divided into comparative studies and single country studies) and TRIPS-plus rules (divided into data exclusivity, patent term extensions and secondary patenting). Most studies focused not on specific trade agreements, but on TRIPS-plus provisions, which can also be found within some trade agreements.
The main finding of this review is that the stronger pharmaceutical monopolies created by TRIPs-plus intellectual property rules are generally associated with increased drug prices, delayed availability and increased costs to consumers and governments. There is evidence that TRIPS flexibilities can facilitate access to medicines although their use is limited to date. There were few studies that included resource poor settings, signalling a need for greater research in such settings where the impact on access to medicines is likely to be more damaging.
Neurofeedback for opioid dependent patients in an outpatient setting: a pilot feasibility study
Authors: Rempala Helena A. , Barterian Justin A. 
Published: April 2022

Abstract
Background
Neurofeedback (NF) has been described as “probably efficacious” when used in conjunction with other interventions for substance use disorders, including the recent studies in the population of individuals with opioid use disorder. Despite these promising outcomes, the seriousness of the opioid epidemic, and the high rate of relapse even with the most effective medication-assisted maintenance treatments NF continues to be an under-researched treatment modality. This article explores factors that affected the feasibility of adding Alpha/Theta Neurofeedback to treatment as usual for opioid dependence in an outpatient urban treatment center. The study strived to replicate previous research completed in Iran that found benefits of NF for opioid dependence.

Methods
Out of approximately two dozen patients eligible for Alpha/Theta NF, about 60% (n = 15) agreed to participate; however, only 2 participants completed treatment. The rates of enrollment in response to active treatment were monitored.

Results
The 4 factors affecting feasibility were: (1) the time commitment required of participants and providers, (2) ineffectiveness of standard incentives to promote participation, (3) delayed effects of training, and (4) the challenges of researching treatment options not reimbursed by the insurance companies.

Conclusions
The findings indicate that a large-scale study examining the use of NF for the treatment of opioid use disorder in the United States will likely be difficult to accomplish without modification to the traditional randomized control study approach and suggests challenges to the implementation of this treatment in an outpatient setting. A single-case methodology is proposed as a viable alternative.
Extending analytic methods for economic evaluation in implementation science
Authors: O’Leary Meghan C. , Hassmiller Lich Kristen , Frerichs Leah , Leeman Jennifer , Reuland Daniel S. , Wheeler Stephanie B. 
Published: April 2022

AbstractBackgroundEconomic evaluations of the implementation of health-related evidence-based interventions (EBIs) are conducted infrequently and, when performed, often use a limited set of quantitative methods to estimate the cost and effectiveness of EBIs. These studies often underestimate the resources required to implement and sustain EBIs in diverse populations and settings, in part due to inadequate scoping of EBI boundaries and underutilization of methods designed to understand the local context. We call for increased use of diverse methods, especially the integration of quantitative and qualitative approaches, for conducting and better using economic evaluations and related insights across all phases of implementation.Main bodyWe describe methodological opportunities by implementation phase to develop more comprehensive and context-specific estimates of implementation costs and downstream impacts of EBI implementation, using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. We focus specifically on the implementation of complex interventions, which are often multi-level, resource-intensive, multicomponent, heterogeneous across sites and populations, involve many stakeholders and implementation agents, and change over time with respect to costs and outcomes. Using colorectal cancer (CRC) screening EBIs as examples, we outline several approaches to specifying the “boundaries” of EBI implementation and analyzing implementation costs by phase of implementation. We describe how systems mapping and stakeholder engagement methods can be used to clarify EBI implementation costs and guide data collection—particularly important when EBIs are complex. In addition, we discuss the use of simulation modeling with sensitivity/uncertainty analyses within implementation studies for projecting the health and economic impacts of investment in EBIs. Finally, we describe how these results, enhanced by careful data visualization, can inform selection, adoption, adaptation, and sustainment of EBIs.ConclusionHealth economists and implementation scientists alike should draw from a larger menu of methods for estimating the costs and outcomes associated with complex EBI implementation and employ these methods across the EPIS phases. Our prior experiences using qualitative and systems approaches in addition to traditional quantitative methods provided rich data for informing decision-making about the value of investing in CRC screening EBIs and long-term planning for these health programs. Future work should consider additional opportunities for mixed-method approaches to economic evaluations.