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BackgroundRisk scores can be useful in clinical risk stratification and accurate allocations of medical resources, helping health providers improve patient care. Point-based scores are more understandable and explainable than other complex models and are now widely used in clinical decision making. However, the development of the risk scoring model is nontrivial and has not yet been systematically presented, with few studies investigating methods of clinical score generation using electronic health records. ObjectiveThis study aims to propose AutoScore, a machine learning–based automatic clinical score generator consisting of 6 modules for developing interpretable point-based scores. Future users can employ the AutoScore framework to create clinical scores effortlessly in various clinical applications. MethodsWe proposed the AutoScore framework comprising 6 modules that included variable ranking, variable transformation, score derivation, model selection, score fine-tuning, and model evaluation. To demonstrate the performance of AutoScore, we used data from the Beth Israel Deaconess Medical Center to build a scoring model for mortality prediction and then compared the data with other baseline models using the receiver operating characteristic analysis. A software package in R 3.5.3 (R Foundation) was also developed to demonstrate the implementation of AutoScore. ResultsImplemented on the data set with 44,918 individual admission episodes of intensive care, the AutoScore-created scoring models performed comparably well as other standard methods (ie, logistic regression, stepwise regression, least absolute shrinkage and selection operator, and random forest) in terms of predictive accuracy and model calibration but required fewer predictors and presented high interpretability and accessibility. The nine-variable, AutoScore-created, point-based scoring model achieved an area under the curve (AUC) of 0.780 (95% CI 0.764-0.798), whereas the model of logistic regression with 24 variables had an AUC of 0.778 (95% CI 0.760-0.795). Moreover, the AutoScore framework also drives the clinical research continuum and automation with its integration of all necessary modules. ConclusionsWe developed an easy-to-use, machine learning–based automatic clinical score generator, AutoScore; systematically presented its structure; and demonstrated its superiority (predictive performance and interpretability) over other conventional methods using a benchmark database. AutoScore will emerge as a potential scoring tool in various medical applications.

BackgroundBipolar disorder (BD) conveys the highest risk of suicide of all mental disorders. We sought to externally validate a risk calculator (RC) of suicide attempts developed in youth with BD from the Course and Outcome of Bipolar Youth (COBY) study in an adult sample.MethodsA prospective cohort of adults with BD from the National Institute of Mental Health Collaborative Depression Study (CDS; N = 427; mean (+/- SD) age at intake (36 +/- 13 years)) was secondarily analyzed to validate the COBY RC for one-year risk of suicide attempts/deaths. Nine of the ten predictor variables from the COBY RC were available in the CDS and used: age, age of mood disorder onset, first and second (partial) degree family history of suicide, history of psychotic symptoms, substance use disorder, prior suicide attempt, socioeconomic status, and non-suicidal self-injury (prospectively, incompletely at baseline).ResultsOver a mean (SD) follow-up of 19 (10) years, 29 % of the CDS sample attempted suicide. The RC predicted suicide attempts/deaths over one-year follow-up with an area under the receiver operating characteristic curve (AUC) of 0.78 (95 % CI 0.75-0.80). The RC performed slightly better in those with a younger age of mood disorder onset.LimitationsClinical samples may limit generalizability; the RC does not assess more acute suicide risk.ConclusionsOne-year risk of suicide attempts/deaths can be predicted with acceptable accuracy in youth and adults with BD, comparable to commonly used RCs to predict cardiovascular risk. This RC may help identify higher-risk individuals with BD for personalized treatment and research. https://cobysuicideattemptsrc.shinyapps.io/Shiny.

Data that influence policy and major investment decisions risk entrenching social and political inequities.

Contributory science—including citizen and community science—allows scientists to leverage participant-generated data while providing an opportunity for engaging with local community members. Data yielded by participant-generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts. The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases. We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation, detectability, sampling and preference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data. Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

Gentle stroking of the skin is a common social touch behavior with positive affective consequences. A preference for slow versus fast stroking of hairy skin has been closely linked to the firing of unmyelinated C-tactile (CT) somatosensory afferents. Because the firing of CT afferents strongly correlates with touch pleasantness, the CT pathway has been considered a social-affective sensory pathway. Recently, ablation of the spinothalamic pathway- thought to convey all C-fiber sensations- in patients with cancer pain impaired pain, temperature, and itch, but not ratings of pleasant touch. This suggested integration of afferent A and CT fiber input in the spinal cord, or mechanoreceptive A-fiber contributions to computations of touch pleasantness in the brain. However, contribution of mechanoreceptive A-fibers to touch pleasantness, in humans without pain, remains unknown. In the current, single-blinded study, we performed two types of peripheral nerve blocks in healthy adults to temporarily eliminate the contribution of A-fibers to touch perception. Our findings show that when mechanoreceptive A-fiber function is greatly diminished, the perceived intensity and pleasantness of both gentle stroking and deep pressure are nearly abolished. These findings demonstrate that explicit perception of the pleasantness of CT-targeted brushing and pressure both critically depend on afferent A-fibers.

BACKGROUND: Extracranial multisystem organ failure is a common sequela of severe traumatic brain injury (TBI). Risk factors for developing circulatory shock and long-term functional outcomes of this patient subset are poorly understood. OBJECTIVE: To identify emergency department predictors of circulatory shock after moderate-severe TBI and examine long-term functional outcomes in patients with moderate-severe TBI who developed circulatory shock. METHODS: We conducted a retrospective cohort study using the Transforming Clinical Research and Knowledge in TBI database for adult patients with moderate-severe TBI, defined as a Glasgow Coma Scale (GCS) score of

Urban agriculture has myriad benefits for those who participate in it, but it's not guaranteed to be more climate-friendly than conventional agriculture. That said, there are some very specific steps urban farmers can take to slash carbon emissions. [ABSTRACT FROM AUTHOR]

To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models-especially N-mixtures with beta-binomial detection submodels-were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models.

Identifying rates at which birders engage with different species can inform the impact and efficacy of conservation outreach and the scientific use of community-collected biodiversity data. Species that are thought to be “charismatic” are often prioritized in conservation, and previous researchers have used sociological experiments and digital records to estimate charisma indirectly. In this study, we take advantage of community science efforts as another record of human engagement with animals that can reveal observer biases directly, which are in part driven by observer preference. We apply a multistage analysis to ask whether opportunistic birders contributing to iNaturalist engage more with larger, more colorful, and rarer birds relative to a baseline approximated from eBird contributors. We find that body mass, color contrast, and range size all predict overrepresentation in the opportunistic dataset. We also find evidence that, across 472 modeled species, 52 species are significantly overreported and 158 are significantly underreported, indicating a wide variety of species-specific effects. Understanding which birds are highly engaging can aid conservationists in creating impactful outreach materials and engaging new naturalists. The quantified differences between two prominent community science efforts may also be of use for researchers leveraging the data from one or both of them to answer scientific questions of interest.

BackgroundTo identify prospectively ascertained individual and family factors that are associated with improvement in Bipolar Disorder (BD) among youths who initially presented with poor course.Methods82 youths with BD with persistent poor mood symptomatology ("predominantly ill course") were compared to 70 youths with BD who at intake had poor course, but showed improvement during the follow-up ("ill with improving course"), (ages 12.3 ± 3.3, vs. 11.7 ± 3.3 years old, at intake). Improvement was measured by the percentage of time euthymic during a mean follow-up of 12.8 years. Youths and parents were interviewed to assess psychopathology, functioning, treatment, and familial functioning and psychopathology.ResultsCompared to the ill group, since intake, the improving group showed significantly lower subthreshold depression and hypo/mania, Attention Deficit Hyperactivity Disorder, and Disruptive Behavior Disorders. Parental Socioeconomic Status (SES) remained unchanged over time in the ill group, but progressively increased in the improving group. Importantly, the change in SES predated the improvement in the mood trajectory. The most influential variables that predicted improvement were higher SES, and absence of parental BD and Substance Use Disorder (SUD). Parental SUD also negatively affected the parental SES, which was directly associated with worse mood course.LimitationsPredominantly self-reported White samples may limit generalizability; other factors potentially associated with outcome (e.g., treatment adherence), were not ascertained.ConclusionsIn addition to treating mood/comorbid psychopathology in symptomatic BD youths, to improve their prognosis, it is crucial to address their parent's BD and SUD and promote parental education/employment.

BACKGROUND: Childhood abuse negatively impacts the course of Bipolar Disorder (BD). Yet, no study has examined risk factors associated with prospectively evaluated physical/sexual abuse, specifically, those preceding first abuse among BD youth. We investigate past/intake/follow-up factors preceding first physical/sexual abuse among BD youth. METHODS: Childhood-onset BD participants (n = 279 youth, mean age at intake = 12, mean length of follow-up = 12 years) enrolled in the Course and Outcome of Bipolar Youth (COBY) study. Demographic, clinical and family history variables were assessed every 7 months on average using Longitudinal Interval Follow-up Evaluation and Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS-PL). Abuse was evaluated at intake using the K-SADS-PL, over follow-up with a Traumatic Events Screen. Family psychopathology was assessed using Family History Screen/Structured Clinical Interview for Diagnostic Statistical Manual-IV. RESULTS: Fifteen-percent of youth reported new-onset abuse during follow-up (62% physical, 26% sexual; 12% both). Intake predictors included more severe depressive symptoms (HR = 1.29), low socioeconomic-status (SES) in families with substance abuse (HR = 0.84) (physical abuse), and female sex (HR = 2.41) (sexual abuse). Follow-up predictors preceding physical abuse included: older age (HR = 1.42), disruptive disorders (HR = 1.39), and the interaction between low SES and family substance abuse (HR = 0.86). For sexual abuse, female sex (HR = 4.33) and a non-biologically related father presence in the household (HR = 2.76). Good relationships with friends (prospectively evaluated) protected against physical/sexual abuse (HR = 0.72/0.70, respectively). LIMITATIONS: Prospective data was gathered longitudinally but assessed retrospectively at every follow-up; perpetrator information and abuse severity were not available. CONCLUSIONS: Identifying factors temporally preceding new onset physical/sexual abuse may hold promise for identifying high-risk youth with BD.

BackgroundEarly hypotension following moderate to severe traumatic brain injury (TBI) is associated with increased mortality and poor long-term outcomes. Current guidelines suggest the use of intravenous vasopressors to support blood pressure following TBI; however, guidelines do not specify vasopressor type, resulting in variation in clinical practice. Minimal data are available to guide clinicians on optimal early vasopressor choice to support blood pressure following TBI. Therefore, we conducted a multicenter study to examine initial vasopressor choice for the support of blood pressure following TBI and its association with clinical and functional outcomes after injury.MethodsWe conducted a retrospective cohort study of patients enrolled in the transforming research and clinical knowledge in traumatic brain injury (TRACK-TBI) study, an 18-center prospective cohort study of patients with TBI evaluated in participating level I trauma centers. We examined adults with moderate to severe TBI (defined as Glasgow Coma Scale score

Background: Clinical neuroimaging studies often investigate group differences between patients and controls, yet multivariate imaging features may enable individual-level classification. This study aims to classify youth with bipolar disorder (BD) versus healthy youth using grey matter cerebral blood flow (CBF) data analyzed with logistic regressions. Methods: Using a 3 Tesla magnetic resonance imaging (MRI) system, we collected pseudo-continuous, arterial spin-labelling, resting-state functional MRI (rfMRI) and Trweighted images from youth with BD and healthy controls. We used 3 logistic regression models to classify youth with BD versus controls, controlling for age and sex, using mean grey matter CBF as a single explanatory variable, quantitative CBF features based on principal component analysis (PCA) or relative (intensity-normalized) CBF features based on PCA. We also carried out a comparison analysis using rfMRI data. Results: The study included 46 patients with BD (mean age 17 yr, standard deviation [SD] 1 yr; 25 females) and 49 healthy controls (mean age 16 yr, SD 2 yr; 24 females). Global mean CBF and multivariate quantitative CBF offered similar classification performance that was above chance. The association between CBF images and the feature map was not significantly different between groups (p = 0.13); however, the multivariate classifier identified regions with lower CBF among patients with BD ([DELTA]CBF = -2.94 mL/100 g/min; permutation test p = 0047). Classification performance decreased when considering rfMRI data. Limitations: We cannot comment on which CBF principal component is most relevant to the classification. Participants may have had various mood states, comorbidities, demographics and medication records. Conclusion: Brain CBF features can classify youth with BD versus healthy controls with above-chance accuracy using logistic regression. A global CBF feature may offer similar classification performance to distinct multivariate CBF features., Introduction Bipolar disorder (BD) is a severe psychiatric condition defined by recurrent episodes of elevated and depressed mood. The onset of BD most commonly occurs during adolescence. (1) Diagnosis of [...]