bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records
Authors: Lu Yuan , Xinxin Du Cindy , Khidir Hazar , Caraballo César , Mahajan Shiwani , Spatz Erica S. , Curry Leslie A. , Krumholz Harlan M. 
Published: February 2023

Background:
The digital transformation of medical data presents opportunities for novel approaches to manage patients with persistent hypertension. We sought to develop an actionable taxonomy of patients with persistent hypertension (defined as 5 or more consecutive measurements of blood pressure ≥160/100 mmHg over time) based on data from the electronic health records.

Methods:
This qualitative study was a content analysis of clinician notes in the electronic health records of patients in the Yale New Haven Health System. Eligible patients were 18 to 85 years and had blood pressure ≥160/100 mmHg at 5 or more consecutive outpatient visits between January 1, 2013 and October 31, 2018. A total of 1664 patients met criteria, of which 200 records were randomly selected for chart review. Through a systematic, inductive approach, we developed a rubric to abstract data from the electronic health records and then analyzed the abstracted data qualitatively using conventional content analysis until saturation was reached.

Results:
We reached saturation with 115 patients, who had a mean age of 66.0 (SD, 11.6) years; 54.8% were female; 52.2%, 30.4%, and 13.9% were White, Black, and Hispanic patients. We identified 3 content domains related to persistence of hypertension: (1) non-intensification of pharmacological treatment, defined as absence of antihypertensive treatment intensification in response to persistent severely elevated blood pressure; (2) non-implementation of prescribed treatment, defined as a documentation of provider recommending a specified treatment plan to address hypertension but treatment plan not being implemented; and (3) non-response to prescribed treatment, defined as clinician-acknowledged persistent hypertension despite documented effort to escalate existing pharmacologic agents and addition of additional pharmacologic agents with presumption of adherence.

Conclusions:
This study presents a novel actionable taxonomy for classifying patients with persistent hypertension by their contributing causes based on electronic health record data. These categories can be automated and linked to specific types of actions to address them.
Associations Between Long-Term Air Pollutant Exposure and 30-Day All-Cause Hospital Readmissions in US Patients With Stroke
Authors: Tran Phoebe M. , Warren Joshua L. , Leifheit Erica C. , Goldstein Larry B. , Lichtman Judith H. 
Published: February 2023

Background:
Long-term exposure to air pollutants is associated with increased stroke incidence, morbidity, and mortality; however, research on the association of pollutant exposure with poststroke hospital readmissions is lacking.

Methods:

We assessed associations between average annual carbon monoxide (CO), nitrogen dioxide (NO
2
), ozone (O
3
), particulate matter 2.5, and sulfur dioxide (SO
2
) exposure and 30-day all-cause hospital readmission in US fee-for-service Medicare beneficiaries age ≥65 years hospitalized for ischemic stroke in 2014 to 2015. We fit Cox models to assess 30-day readmissions as a function of these pollutants, adjusted for patient and hospital characteristics and ambient temperature. Analyses were then stratified by treating hospital performance on the Centers for Medicare and Medicaid Services risk-standardized 30-day poststroke all-cause readmission measure to determine if the results were independent of performance: low (Centers for Medicare and Medicaid Services rate for hospital <25th percentile of national rate), high (>75th percentile), and intermediate (all others).

Results:

Of 448 148 patients with stroke, 12.5% were readmitted within 30 days. Except for tropospheric NO
2
(no national standard), average 2-year CO, O
3
, particulate matter 2.5, and SO
2
values were below national limits. Each one SD increase in average annual CO, NO
2
, particulate matter 2.5, and SO
2
exposure was associated with an adjusted 1.1% (95% CI, 0.4–1.9%), 3.6% (95% CI, 2.9%–4.4%), 1.2% (95% CI, 0.2%–2.3%), and 2.0% (95% CI, 1.1%–3.0%) increased risk of 30-day readmission, respectively, and O
3
with a 0.7% (95% CI, 0.0%–1.5%) decrease. Associations between long-term air pollutant exposure and increased readmissions persisted across hospital performance categories.

Conclusions:
Long-term air pollutant exposure below national limits was associated with increased 30-day readmissions after stroke, regardless of hospital performance category. Whether air quality improvements lead to reductions in poststroke readmissions requires further research.
Conditioning induced placebo-like and nocebo-like effects of thermal discomfort in adults but not in youth
Authors: Weik Ella , Neuenschwander Regula , Edgington Brinn , Jensen Karin , Tipper Christine M , Oberlander Tim F 
Published: February 2023

IntroductionConditioning can be used to modulate the perception of pain, in the form of placebo and nocebo effects. Previous studies show inconsistent results as to whether adolescents show similar, weaker, or non-significant conditioned placebo and nocebo effects compared to effects found in adults. There are suggestions that such differences (if any) may dependent on the cues used in the thermal conditioning paradigms. Therefore, in this current study, we utilized novel, neutral 3D-shaped visual cues to implicitly induce conditioned placebo-like and nocebo-like effects in adolescents and adults.MethodsDuring the conditioning paradigm, distinct cues (Fribbles) were paired with low and high temperatures in 24 adults and 20 adolescents (mean age = 25.5 years). In the testing phase, these conditioned cues as well as a neutral (unconditioned) cue were presented with moderate temperatures.ResultsThermal discomfort of moderate temperatures was lower when presented with the conditioned low heat cue (placebo-like effect) and higher when thermal stimuli were presented with the high heat cue (nocebo-like effect) compared to the neutral cue. The effects were driven by adults, as neither the placebo-like nor the nocebo-like effect was significant in adolescents. The difference between adolescents and adults was not explained by differences in temperature or discomfort levels, as adults and adolescents had comparable calibrated temperatures and levels of discomfort during heat stimuli.ConclusionOur findings suggest that thermal perception in adolescents is less influenced by conditioning to an engaging novel visual cue, compared to adults. Our work may have implications for better understanding the scope and limitations of conditioning as a key mechanism of placebo and nocebo effects in youth.
Latina mothers’ perspectives on adverse experiences and protection of Latinx youth in an agricultural community
Authors: Parmar Deepika D. , Minnis Alexandra M. , Caballero Elodia , Zerofsky Melissa , Comfort Megan , Raymond-Flesch Marissa 
Published: February 2023

Abstract
Background
Adverse Childhood Experiences (ACEs) are a measure of childhood toxic stress that have a dose-dependent relationship with many adult health outcomes. While ACEs have been validated across diverse populations to measure neglect, abuse, and family dysfunction, they do not specifically assess trauma related to racism/xenophobia and immigration. 54% of Latinx youth in the United States are immigrants or children of immigrants and a large group with potentially unmeasured trauma. This study looks beyond ACEs to identify adverse and protective factors for healthy development among Latinx youth in an agricultural community through the perspectives of their mothers.

Methods
Twenty mothers of adolescent participants in A Crecer: the Salinas Teen Health Study (a prospective cohort study of 599 adolescents) completed semi-structured interviews in Spanish. Interviews focused on mothers’ perspectives on community resources, parenting strategies, parenting support systems, and their future aspirations for their children. Four coders completed iterative rounds of thematic coding drawing from published ACEs frameworks (original ACEs, community ACEs) and immigrant specific adverse events arising from the data.

Results
Mothers in this study reported adverse experiences captured within community-level ACEs but also distinct experiences related to intergenerational trauma and immigrant-related adversities. The most cited community-level ACEs were housing instability and community violence. Immigrant related adversities included experiences of systemic racism with loss of resources, political instability limiting structural resources, and language-limited accessibility. These were exacerbated by the loss of family supports due to immigration related family-child separation including deportations and staggered parent–child migration. Having experienced intergenerational trauma and systemic oppression, mothers discussed their strategies for building family unity, instilling resilience in their children, and improving socioeconomic opportunities for their family.

Conclusions
Latina mothers shared the impacts of immigrant-related experiences on systemic inequities in the United States which are currently missing from the ACEs framework. Immigrant specific adverse events include language-limited accessibility, or family-child separations, and policies impacting structural resources for immigrant families. Mothers highlighted their capacity to build resilience in their children and buffer impacts of systemic racism. Community-tailored interventions can build on this foundation to reduce health disparities and promote health equity in this population.
Exploring predictors of welfare dependency 1, 3, and 5 years after mental health-related absence in danish municipalities between 2010 and 2012 using flexible machine learning modelling
Authors: Bjerregaard Søren Skotte 
Published: February 2023

Abstract
Background
Using XGBoost (XGB), this study demonstrates how flexible machine learning modelling can complement traditional statistical modelling (multinomial logistic regression) as a sensitivity analysis and predictive modelling tool in occupational health research.

Design
The study predicts welfare dependency for a cohort at 1, 3, and 5 years of follow-up using XGB and multinomial logistic regression (MLR). The models’ predictive ability is evaluated using tenfold cross-validation (internal validation) and geographical validation (semi-external validation). In addition, we calculate and graphically assess Shapley additive explanation (SHAP) values from the XGB model to examine deviation from linearity assumptions, including interactions. The study population consists of all 20–54 years old on long-term sickness absence leave due to self-reported common mental disorders (CMD) between April 26, 2010, and September 2012 in 21 (of 98) Danish municipalities that participated in the Danish Return to Work program. The total sample of 19.664 observations is split geospatially into a development set (n = 9.756) and a test set (n = 9.908).

Results
There were no practical differences in the XGB and MLR models’ predictive ability. Industry, job skills, citizenship, unemployment insurance, gender, and period had limited importance in predicting welfare dependency in both models. On the other hand, welfare dependency history and reason for sickness absence were strong predictors. Graphical SHAP-analysis of the XGB model did not indicate substantial deviations from linearity assumptions implied by the multinomial regression model.

Conclusion
Flexible machine learning models like XGB can supplement traditional statistical methods like multinomial logistic regression in occupational health research by providing a benchmark for predictive performance and traditional statistical models' ability to capture important associations for a given set of predictors as well as potential violations of linearity.

Trial registration
ISRCTN43004323.
COVID-19 vaccine uptake and hesitation among men and women preparing for pregnancy: a cross-section survey based on the theory of planned behavior
Authors: Lei Anjiang , Xi Chunyang , Luo Xiaoxue , Pu Yan , You Huaxuan 
Published: February 2023

Abstract
Background
Given the accelerated speed of COVID-19 vaccine research and administration, the main barriers to herd immunity appear to be concerns about safety and efficacy. Men and women preparing for pregnancy may have the same concerns about COVID-19 vaccination, but few studies have focused on COVID-19 vaccine uptake and hesitation among them.

Methods
A cross-sectional study was conducted among men and women who were preparing for pregnancy in Southwest China. The questionnaire was designed based on the theory of planned behavior (TPB). Multiple logistic regression was used to explore the determinants of the behaviors of COVID-19 vaccination.

Results
A total of 2878 participants completed the survey. A total of 53.89% of participants received at least one dose of the COVID-19 vaccine. A total of 45.21% of participants would receive the COVID-19 vaccine in the future. A total of 0.90% of participants never thought about receiving the COVID-19 vaccine. Multiple logistic regression model 1 showed that female participants (OR:5.497, 95%CI: 4.292–7.041), participants who never received influenza vaccine (OR:2.664, 95%CI: 1.908–3.718), participants who had never been tested for COVID-19 (OR:2.244, 95%CI:1.504–3.349), participants who had higher score of negative attitude (OR:1.448, 95%CI: 1.219–1.719), participants who had lower scores of injunctive norms (OR:0.440, 95%CI: 0.360–0.537) and descriptive norms (OR:0.105, 95%CI: 0.088–0.126) were more likely to delay COVID-19 vaccination. Model 2 showed that participants who had lower scores for positive attitude (OR: 0.406, 95% CI: 0.230–0.716), injunctive norms (OR: 0.283, 95% CI: 0.130–0.614) and descriptive norms (OR: 0.060, 95% CI: 0.038–0.094) were more likely to refuse COVID-19 vaccination.

Conclusions
The COVID-19 vaccination rate of men and women preparing for pregnancy was significantly lower than the average vaccination rate of China. Gender, protective health behaviors, vaccination attitudes, and subjective norms had effects on the vaccination behaviors of couples preparing for pregnancy.
Study on the correlation between the severity of patellofemoral arthritis and the morphology of the distal femur
Authors: Fan Chongyi , Niu Yingzhen , Wei Maozheng , Kong Lingce , Wang Fei 
Published: February 2023

Abstract
Purpose
Distal femoral torsion is a key factor for poor alignment of patellofemoral joint. This study aims to evaluate the correlation between distal femoral torsion and the severity of patellofemoral arthritis, and to analyze the correlation between distal femoral torsion and the morphology of femoral condyle.

Methods
A retrospective analysis was performed on 125 patients awaiting surgical treatment for knee osteoarthritis from January 2021 to March 2022(79 females, 46 males, average age: 65.78 years, SD 6.61). All patients underwent knee joint radiography, lower-limb digital radiography, and knee joint CT scans. The ratio of length of each distal femoral condyle, TT-TG, patellar tilt, DFL-PCL, DFL-TEA, TEA-PCL and TEA-ACL were measured. The Pearson correlation coefficient was used to evaluate the correlation between distal femoral torsion and ratio of distal femoral condyle, TT-TG and patellar tilt. Logistic regression was used to evaluate the correlation between each parameter and the severity of PFOA.

Results
With the increased severity of PFOA, TT-TG, patellar tilt, DFL-PCL, DFL-TEA and PCA all tended to increase. Patellar tilt was correlated with DFL-PCL (r = 0.243) and TEA-PCL(r = 0.201), but TT-TG had no evident correlation with distal femoral torsion. Compared with Grade I patients of PFOA, DFL-PCL, DFL-TEA, and TEA-PCL were risk factors for increased severity of patellofemoral arthritis in Grade III patients of PFOA, but there was no significant statistic difference in Grade II patients of PFOA.

Conclusions
Distal femoral torsion correlates with the severity of patellofemoral arthritis. Variation of the femoral transepicondylar axis caused by the change of ratio of the femoral condyle is particularly important in the distal femoral torsion. In patients with severe PFOA, abnormal variation of the femoral condyle axis should be not ignored.
Comparison of physicians’ and dentists’ incident reports in open data from the Japan Council for Quality Health Care: a mixed-method study
Authors: Akiyama Naomi , Akiyama Tomoya , Sato Hideaki , Shiroiwa Takeru , Kishi Mitsuo 
Published: February 2023

Abstract
Background
Patient safety is associated with patient outcomes. However, there is insufficient evidence of patient safety in the dental field. This study aimed to compare incidents reported by dentists and physicians, compare the type of errors made by them, and identify how dentists prevent dental errors.

Methods
A mixed-methods study was conducted using open data from the Japan Council for Quality Health Care database. A total of 6071 incident reports submitted for the period 2016–2020 were analyzed; the number of dentists’ incident reports was 144, and the number of physicians’ incident reports was 5927.

Results
The percentage of dental intern reporters was higher than that of medical intern reporters (dentists: n = 12, 8.3%; physicians: n = 180, 3.0%; p = 0.002). The percentage of reports by dentists was greater than that by physicians: wrong part of body treated (dentists: n = 26, 18.1%; physicians: n = 120, 2.0%; p < 0.001), leaving foreign matter in the body (dentists: n = 15, 10.4%; physicians: n = 182, 3.1%; p < 0.001), and accidental ingestion (dentists: n = 8, 5.6%; physicians: n = 8, 0.1%; p < 0.001), and aspiration of foreign body (dentists: n = 5, 3.4%; physicians: n = 33, 0.6%; p = 0.002). The percentage of each type of prevention method utilized was as follows: software 27.8% (n = 292), hardware (e.g., developing a new system) 2.1% (n = 22), environment (e.g., coordinating the activities of staff) 4.2% (n = 44), liveware (e.g., reviewing procedure, double checking, evaluating judgement calls made) 51.6% (n = 542), and liveware-liveware (e.g., developing adequate treatment plans, conducting appropriate postoperative evaluations, selecting appropriate equipment and adequately trained medical staff) 14.3% (n = 150).

Conclusion
Hardware and software and environment components accounted for a small percentage of the errors made, while the components of liveware and liveware-liveware errors were larger. Human error cannot be prevented by individual efforts alone; thus, a systematic and holistic approach needs to be developed by the medical community.
Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis
Authors: Tavazzi Erica , Gatta Roberto , Vallati Mauro , Cotti Piccinelli Stefano , Filosto Massimiliano , Padovani Alessandro , Castellano Maurizio , Di Camillo Barbara 
Published: February 2023

Abstract
Background
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients’ quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients’ characteristics.

Methods
We consider data collected in two distinct data sources, namely the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset and a real-world clinical register (ALS–BS) including data of patients followed up in two tertiary clinical centers of Brescia (Italy). With a focus on the functional abilities progressively impaired as the disease progresses, we use two Process Discovery methods, namely the Directly-Follows Graph and the CareFlow Miner, to mine the population disease trajectories on the PRO-ACT dataset. We characterize the impairment trajectories in terms of patterns, timing, and probabilities, and investigate the effect of some patients’ characteristics at onset on the followed paths. Finally, we perform a comparative study of the impairment trajectories mined in PRO-ACT versus ALS–BS.

Results
We delineate the progression pathways on PRO-ACT, identifying the predominant disabilities at different stages of the disease: for instance, 85% of patients enter the trials without disabilities, and 48% of them experience the impairment of Walking/Self-care abilities first. We then test how a spinal onset increases the risk of experiencing the loss of Walking/Self-care ability as first impairment (52% vs. 27% of patients develop it as the first impairment in the spinal vs. the bulbar cohorts, respectively), as well as how an older age at onset corresponds to a more rapid progression to death. When compared, the PRO-ACT and the ALS–BS patient populations present some similarities in terms of natural progression of the disease, as well as some differences in terms of observed trajectories plausibly due to the trial scheduling and recruitment criteria.

Conclusions
We exploited PM to provide an overview of the evolution scenarios of an ALS trial population and to preliminary compare it to the progression observed in a clinical cohort. Future work will focus on further improving the understanding of the disease progression mechanisms, by including additional real-world subjects as well as by extending the set of events considered in the impairment trajectories.
Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation
Authors: de Capretz Pontus Olsson , Björkelund Anders , Björk Jonas , Ohlsson Mattias , Mokhtari Arash , Nyström Axel , Ekelund Ulf 
Published: February 2023

Abstract
Aims
In the present study, we aimed to evaluate the performance of machine learning (ML) models for identification of acute myocardial infarction (AMI) or death within 30 days among emergency department (ED) chest pain patients.

Methods and results
Using data from 9519 consecutive ED chest pain patients, we created ML models based on logistic regression or artificial neural networks. Model inputs included sex, age, ECG and the first blood tests at patient presentation: High sensitivity TnT (hs-cTnT), glucose, creatinine, and hemoglobin. For a safe rule-out, the models were adapted to achieve a sensitivity > 99% and a negative predictive value (NPV) > 99.5% for 30-day AMI/death. For rule-in, we set the models to achieve a specificity > 90% and a positive predictive value (PPV) of > 70%. The models were also compared with the 0 h arm of the European Society of Cardiology algorithm (ESC 0 h); An initial hs-cTnT < 5 ng/L for rule-out and ≥ 52 ng/L for rule-in. A convolutional neural network was the best model and identified 55% of the patients for rule-out and 5.3% for rule-in, while maintaining the required sensitivity, specificity, NPV and PPV levels. ESC 0 h failed to reach these performance levels.

Discussion
An ML model based on age, sex, ECG and blood tests at ED arrival can identify six out of ten chest pain patients for safe early rule-out or rule-in with no need for serial blood tests. Future studies should attempt to improve these ML models further, e.g. by including additional input data.