bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Evaluation of 2 Work Experience Approaches for Predominantly Black Youth in a Rural Community: Implications for Public Health Workforce Development
Authors: Frerichs Leah , Smith Cambray , Hoover Stephanie , Robinson-Ezekwe Nicole , Khanna Anisha , Ellerby Brian , Joyner Linda , Wynn Mysha , Wrenn Jonina , Stith Doris , Tessler Lindau Stacy , Corbie Giselle 
Published: November 2022

Context:
In order to create a more diverse workforce, there is a need to involve historically excluded youth in public health–related work. Youth involvement in asset-based work experience approaches is especially relevant for rural areas with workforce shortages.

Objective:
To explore the public health workforce development implications of community-based career exploration and asset mapping work experience from the perspective of Black youth.

Design:
We used qualitative in-depth interviews with youth, aged 14 to 22 years, who participated in a work experience program anchored in several rural counties in southeastern United States. A phenomenological lens was applied for qualitative analyses with iterative, team-based data coding. Participants were also surveyed pre- and postprogram to supplement findings.

Program:
A rural community-based organization's work experience program consisted of 2 tracks: (1) Youth Connect—a career exploration track that included work placement within community agencies; and (2) MAPSCorps—a track that employs youth to conduct asset mapping for their community.

Results:
We interviewed 28 of 31 total participants in the 2 tracks. We uncovered 4 emergent profiles in how youth described shifts in their perceptions of community: (1) Skill Developers; (2) Community Questioners; (3) Community Observers; and (4) Community Enthusiasts. In comparing between tracks, youth who participated in work experience that involved asset mapping uniquely described increased observation and expanded view of community resources and had greater increases in research self-efficacy than youth who participated only in career exploration.

Conclusion:
Asset mapping work experience that is directly placed in rural communities can expose Black youth to and engage them in essential public health services (assessing and mobilizing community assets) that impact their community. This type of program, directly integrated into rural communities rather than placed near academic centers, could play a role in creating a more diverse public health workforce.
Low Preoperative Serum Creatinine is Common and Associated With Poor Outcomes After Nonemergent Inpatient Surgery
Authors: Loria Anthony , Glance Laurent G. , Melucci Alexa D. , Boodry Courtney , Justiniano Carla F. , Dunne Richard F. , Mustian Karen M. , Becerra Adan Z. , Jusko Todd A. , Temple Larissa K. , Fleming Fergal J. 
Published: November 2022

Objective:
To assess the association between low preoperative serum creatinine and postoperative outcomes.

Background:
The association between low creatinine and poor surgical outcomes is not well understood.

Methods:
We identified patients with creatinine in the 7 days preceding nonemergent inpatient surgery in the American College of Surgeons National Surgical Quality Improvement Program database from 2005 to 2020. Multivariable logistic regression was used to examine the association between creatinine and 30-day mortality and major complications.

Results:
Of 1,809,576 patients, 27.8% of males and 23.5% of females had low preoperative serum creatinine, 14.6% experienced complications, and 1.2% died. For males, compared with the reference creatinine of 0.85 to 1.04, those with serum creatinine ≤0.44 had 55% increased odds of mortality [ adjusted odds ratio (aOR), 1.55; 95% CI, 1.29-1.86] and 82% increased odds of major complications (aOR, 1.82; 95% CI, 1.69-1.97). Similarly, for females, compared with the reference range of 0.65 to 0.84, those with serum creatinine ≤0.44 had 49% increased odds of mortality (aOR, 1.49; 95% CI, 1.32-1.67) and 76% increased odds of major complications (aOR, 1.76; 95% CI, 1.70-1.83). These associations persisted for the total cohort, among those with mildly low albumin, and for those with creatinine values measured 8 to 30 days preoperatively.

Conclusions:
A low preoperative creatinine is common and associated with poor outcomes after nonemergent inpatient surgery. A low creatinine may help identify high-risk patients who may benefit from further evaluation and optimization.
Routine Assessment of Surgical Resident Wellness-Related Concerns During Biannual Review
Authors: Siu Margaret , Tashjian David B , Fernandez Gladys L , Isotti Joy , Seymour Neal E 
Published: November 2022

Background:
Surgery residency confers stress burdens on trainees. To monitor and mitigate areas of concerns, our education team implemented a six-item biannual survey querying potential stressors. We reviewed the initial five-year experience to assess for trends and improve efforts in maintaining well-being.

Methods:
Surgery residents from all postgraduate years were asked to complete a survey of common concerns, prioritizing them in order of importance. Ranked items were 1) needs of family/friends, 2) nonwork time for study, 3) financial concerns, 4) personal well-being needs, 5) concerns for clinical performance, and 6) administrative demands. Changes were trended over ten review periods. Results were analyzed using Kruskal-Wallis test.

Results:
333 surveys were completed, rendering a 96.5% completion rate. Rankings changed significantly for nonwork time for study (p=0.04), personal well-being needs (p=0.03) and concerns for clinical performance (p=0.004). Nonwork time for study and concerns on clinical performance were consistently ranked as top two stressors over study period, except for Spring 2020. Personal well-being needs ranked highest in Spring 2020; 41% of residents placed this as top two rankings. A decrease in concerns for clinical performance was observed in Spring 2020, corresponding to the COVID-19 pandemic emergency declaration.

Conclusion:
Surgery residents generally prioritized time for study and concerns for assessment of clinical performance as highest areas of concern. With the occurrence of a pandemic, increased prioritization of personal well-being was observed. Used routinely with biannual reviews, the survey was able to identify plausible changes in resident concerns. Determination of levels of actual stress and actual association with the pandemic requires additional study.
Loneliness and mood disorders: consequence, cause and/or unholy alliance?
Authors: Giacco Domenico 
Published: November 2022

Purpose of review
People with persistent depressive disorders and with bipolar disorder are more likely to feel lonely than people in the general population. This evidence update focused on studies in the last 2 years, characterized by the COVID-19 pandemic and consequent social distancing directives.

Recent findings
Longitudinal studies identified that people who feel lonely are more likely to become depressed or to experience relapse of mood disorders. There is emerging evidence that feelings of loneliness or mandatory social isolation can precede manic episodes. Hence the relationship between loneliness and mood disorders is complex and bidirectional. Interventions were developed to reduce loneliness in people with mental health problems, including depressive disorders, through cognitive modification and/or supported socialisation. No loneliness-focused interventions have been specifically tailored to people with bipolar disorder.

Summary
Studies carried out before and during the COVID-19 pandemic found that feelings of loneliness can be both consequences and precursors of persistent depression and bipolar disorder. Mood symptoms and loneliness have a cumulative negative effect on physical and mental health outcomes. Conceptual overlaps and relations between loneliness and mood symptoms should be clarified in qualitative studies. Theory-driven intervention models should be developed and tested in methodologically robust studies.
Neurostimulation as a treatment for mood disorders in patients: recent findings
Authors: Afshari Masoud , Belzung Catherine , Bloch Solal 
Published: November 2022

Purpose of review
The use of neurostimulation to treat mood disorders dates back to the 1930s. Recent studies have explored various neurostimulation methods aimed at both restoring a healthy brain and reducing adverse effects in patients. The purpose of this review is to explore the most recent hypotheses and clinical studies investigating the effects of stimulating the brain on mood disorders.

Recent findings
Recent work on brain stimulation and mood disorders has focused mainly on three aspects: enhancing efficacy and safety by developing new approaches and protocols, reducing treatment duration and chances of relapse, and investigating the physiological and pathological mechanisms behind treatment outcomes and possible adverse effects.
This review includes some of the latest studies on both noninvasive techniques, such as transcranial magnetic stimulation, magnetic seizure therapy, transcranial direct current stimulation, transcranial alternating current stimulation, electroconvulsive treatment, and invasive techniques, such as deep brain stimulation and vagus nerve stimulation.

Summary
Brain stimulation is widely used in clinical settings; however, there is a lack of understanding about its neurobiological mechanism. Further studies are needed to understand the neurobiology of brain stimulation and how it can be used to treat mood disorders in their diversity, including comorbidities with other illnesses.
Population-specific facial traits and diagnosis accuracy of genetic and rare diseases in an admixed Colombian population
Authors: Echeverry Luis Miguel , Candelo Estephania , Gómez Eidith , Solís Paula , Ramírez Diana , Ortiz Diana , González Alejandro , Sevillano Xavier , Cuéllar Juan Carlos , Pachajoa Harry , Martínez-Abadías Neus 
Published: November 2022

ABSTRACTUp to 40% of genetic and rare disorders (RD) present facial dysmorphologies. Visual assessment of facial gestalt is commonly used for clinical diagnosis, health management and treatment monitoring. Quantitative approaches to facial phenotypes are more objective and provide first diagnoses of RD with relatively high accuracy, but are mainly based on populations of European descent, disregarding the influence of population ancestry. Here we assessed the facial phenotypes associated to four genetic disorders in a Latino-American population from Colombia. We recorded the coordinates of 18 facial landmarks in 2D images from 79 controls 51 pediatric individuals diagnosed with Down (DS), Morquio (MS), Noonan (NS) and Neurofibromatosis type 1 (NF1) syndromes. We quantified facial differences using Euclidean Distance Matrix Analysis (EDMA) and assessed the diagnostic accuracy of Face2gene, an automatic deep learning algorithm with widespread use in the clinical practice.Quantitative comparisons indicated that individuals diagnosed with DS and MS were associated with the most severe phenotypes, with 58.2% and 65.4% of facial traits significantly different as compared to controls. The percentage decreased to 47.7% in NS and to 11.4% in NF1. Each syndrome presented a characteristic pattern of facial dysmorphology, supporting the potential of facial biomarkers for disorder diagnosis. However, our results detected population-specific traits in the Colombian population as compared to the facial gestalt described in literature for DS, NS and NF1. When clinical diagnosis based on genetic testing was used to verify the diagnosis based on 2D facial pictures, our results showed that Face2Gene accuracy was very high in DS, moderate in NS and NF1, and very low in MS, with low gestalt similarity scores in highly admixed individuals. Our study underscores the added value of precise quantitative comparison of facial dysmorphologies in genetic and rare disorders and the need to incorporate populations with diverse contributions of Amerindian, African and European ancestry components to further improve automatic diagnostic methods.
Activation of the renin-angiotensin-aldosterone system is attenuated in hypertensive compared to normotensive pregnancy
Authors: Shoemaker Robin , Poglitsch Marko , Huang Hong , Vignes Katherine , Srinivasan Aarthi , Cockerham Cynthia , Schadler Aric , Bauer John A. , O’Brien John M. 
Published: November 2022

AbstractHypertension during pregnancy increases the risk for adverse maternal and fetal outcomes, but mechanisms of pregnancy hypertension are not precisely understood. Elevated plasma renin activity and aldosterone concentrations play an important role in the normal physiologic adaptation to pregnancy. These effectors are reduced in patients with pregnancy hypertension, creating an opportunity to define features of the renin-angiotensin-aldosterone system (RAAS) that are characteristic of this disorder. In the current study, we used a novel LC-MS/MS-based methodology to develop comprehensive profiles of RAAS peptides and effectors over gestation in a cohort of n=74 pregnant women followed prospectively for the development of gestational hypertension and pre-eclampsia (HYP, n=27) versus remaining normotensive (NT, n=47). In NT pregnancy, the plasma renin activity surrogate, (PRA-S, calculated from the sum of [angiotensin I] + [angiotensin II) and aldosterone concentrations significantly increased from first to third trimester, accompanied by a modest increase in concentrations of angiotensin peptide metabolites. In contrast, in HYP pregnancies PRA-S and angiotensin peptides were largely unchanged over gestation, and third trimester aldosterone concentrations were significantly lower compared to NT pregnancies. Results indicate that the predominate features of pregnancies that develop HYP are stalled or waning activation of the RAAS in the second half of pregnancy (accompanied by unchanging levels of angiotensin peptides) and attenuated secretion of aldosterone.
Algorithm for Predicting Valvular Heart Disease from Heart Sounds in an Unselected Cohort
Authors: Waaler Per Niklas , Melbye Hasse , Schirmer Henrik , Johnsen Markus Kreutzer , Dønnem Tom , Ravn Johan , Andersen Stian , Davidsen Anne Herefoss , Aviles-Solis Juan Carlos , Stylidis Michael , Bongo Lars Ailo 
Published: November 2022

ABSTRACTBackgroundAlthough neural networks have shown promise in classifying pathological heart sounds (HS), algorithms have so far either been trained or tested on selected cohorts which can result in selection bias. Herein, the main objective is to explore the ability of neural networks to predict valvular heart disease (VHD) from recordings in an unselected cohort.Methods and resultsUsing annotated HSs and echocardiogram data from 2124 subjects from the Tromsø 7 study, we trained a recurrent neural network to predict murmur grade, which was subsequently used to predict VHD. Presence of aortic stenosis (AS) was detected with sensitivity 90.9%, specificity 94.5%, and area-under-the-curve (AUC) 0.979 (CI:0.963-0.995). At least moderate AS was detected with AUC 0.993 (CI:0.989-0.997). Moderate or greater aortic and mitral regurgitation (AR and MR) were predicted with AUC 0.634 (CI:0.565-703) and 0.549 (CI:0.506-0.593) respectively, which increased to 0.766 and 0.677 when adding clinical variables as predictors.Excluding asymptomatic cases from the positive class increased sensitivity to AR from 54.9% to 85.7%, and sensitivity to MR from 55.6% to 83.3%. Screening jointly for at least moderate regurgitation or presence of stenosis resulted in detection of 54.1% of positive cases, 60.5% of negative cases, 97.7% of AS cases (n=44), and all 12 MS cases.ConclusionsDespite the cohort being unselected, the algorithm detected AS with performance exceeding performance achieved in similar studies based on selected cohorts. Detection of AR and MR based on HS audio was unreliable, but sensitivity was considerably higher for symptomatic cases, and inclusion of clinical variables improved prediction significantly.
Heterogeneity in network structure switches the dominant transmission mode of infectious diseases
Authors: Kollepara Pratyush K. , Chisholm Rebecca H. , Miller Joel C. 
Published: November 2022

AbstractSeveral recent emerging diseases have exhibited both sexual and non-sexual transmission modes (Ebola, Zika and mpox). In the recent mpox outbreaks, transmission through sexual contacts appears to be the dominant mode of transmission. Motivated by this, we use an SIR-like model, to argue that an initially dominant sexual transmission mode can be overtaken by casual transmission at later stages, even if the basic casual reproduction number is less than one. Our results highlight the risk of intervention designs which are informed only by the early dynamics of the disease.Significance StatementThe purpose of this article is to explore the risk from secondary transmission routes of diseases which spread through sexual contact. This is important because infectious diseases such as Ebola, Zika and mpox spread through both sexual transmission and other modes of transmission. Our results suggest that a secondary transmission route which is not dominant in the initial stages, can significantly alter the course of the epidemic and lead to more infections than expected in the later stages of the epidemic.
Natural Language Processing for Clinical Laboratory Data Repository Systems: Implementation and Evaluation for Respiratory Viruses
Authors: Dolatabadi Elham , Chen Branson , Buchan Sarah A. , Marchand-Austin Alex , Azimaee Mahmoud , McGeer Allison J. , Mubareka Samira , Kwong Jeffrey C. 
Published: November 2022

AbstractBackgroundWith the growing volume and complexity of laboratory repositories, it has become tedious to parse unstructured data into structured and tabulated formats for secondary uses such as decision support, quality assurance, and outcome analysis. However, advances in Natural Language Processing (NLP) approaches have enabled efficient and automated extraction of clinically meaningful medical concepts from unstructured reports.ObjectiveIn this study, we aimed to determine the feasibility of using the NLP model for information extraction as an alternative approach to a time-consuming and operationally resource-intensive handcrafted rule-based tool. Therefore, we sought to develop and evaluate a deep learning-based NLP model to derive knowledge and extract information from text-based laboratory reports sourced from a provincial laboratory repository system.MethodsThe NLP model, a hierarchical multi-label classifier, was trained on a corpus of laboratory reports covering testing for 14 different respiratory viruses and viral subtypes. The corpus included 85kunique laboratory reports annotated by eight Subject Matter Experts (SME). The model’s performance stability and variation were analyzed across fine-grained and coarse-grained classes. Moreover, the model’s generalizability was also evaluated internally and externally on various test sets.ResultsThe NLP model was trained several times with random initialization on the development corpus, and the results of the top ten best-performing models are presented in this paper. Overall, the NLP model performed well on internal, out-of-time (pre-COVID-19), and external (different laboratories) test sets with micro-averaged F1 scores >94% across all classes. Higher Precision and Recall scores with less variability were observed for the internal and pre-COVID-19 test sets. As expected, the model’s performance varied across categories and virus types due to the imbalanced nature of the corpus and sample sizes per class. There were intrinsically fewer classes of viruses beingdetectedthan thosetested; therefore, the model’s performance (lowest F1-score of 57%) was noticeably lower in the “detected” cases.ConclusionsWe demonstrated that deep learning-based NLP models are promising solutions for information extraction from text-based laboratory reports. These approaches enable scalable, timely, and practical access to high-quality and encoded laboratory data if integrated into laboratory information system repositories.
Development of an Interactive Web Dashboard to Facilitate the Reexamination of Pathology Reports for Instances of Underbilling of CPT Codes
Authors: Greenburg Jack , Lu Yunrui , Lu Shuyang , Kamau Uhuru , Hamilton Robert , Pettus Jason , Preum Sarah , Vaickus Louis , Levy Joshua 
Published: November 2022

AbstractCurrent Procedural Terminology Codes is a numerical coding system used to bill for medical procedures and services and crucially, represents a major reimbursement pathway. Given that Pathology services represent a consequential source of hospital revenue, understanding instances where codes may have been misassigned or underbilled is critical. Several algorithms have been proposed that can identify improperly billed CPT codes in existing datasets of pathology reports. Estimation of the fiscal impacts of these reports requires a coder (i.e., billing staff) to review the original reports and manually code them again. As the re-assignment of codes using machine learning algorithms can be done quickly, the bottleneck in validating these reassignments is in this manual re-coding process, which can prove cumbersome. This work documents the development of a rapidly deployable dashboard for examination of reports that the original coder may have misbilled. Our dashboard features the following main components: 1) a bar plot to show the predicted probabilities for each CPT code, 2) an interpretation plot showing how each word in the report combines to form the overall prediction, 3) a place for the user to input the CPT code they have chosen to assign. This dashboard utilizes the algorithms developed to accurately identify CPT codes to highlight the codes missed by the original coders. In order to demonstrate the function of this web application, we recruited pathologists to utilize it to highlight reports that had codes incorrectly assigned. We expect this application to accelerate the validation of reassigned codes through facilitating rapid review of false positive pathology reports. In the future, we will use this technology to review thousands of past cases in order to estimate the impact of underbilling has on departmental revenue.