Promoting racial equity in COVID-19 resource allocation.

Due to COVID-19's strain on health systems across the globe, triage protocols determine how to allocate scarce medical resources with the worthy goal of maximising the number of lives saved. However, due to racial biases and long-standing health inequities, the common method of ranking patients based on impersonal numeric representations of their morbidity is associated with disproportionately pronounced racial disparities. In response, policymakers have issued statements of solidarity. However, translating support into responsive COVID-19 policy is rife with complexity. Triage does not easily lend itself to race-based exceptions. Reordering triage queues based on an individual patient's racial affiliation has been considered but may be divisive and difficult to implement. And while COVID-19 hospital policies may be presented as rigidly focused on saving the most lives, many make exceptions for those deemed worthy by policymakers such as front-line healthcare workers, older physicians, pregnant women and patients with disabilities. These exceptions demonstrate creativity and ingenuity-hallmarks of policymakers' abilities to flexibly respond to urgent societal concerns-which should also be extended to patients of colour. This paper dismantles common arguments against the confrontation of racial inequity within COVID-19 triage protocols, highlights concerns related to existing proposals and proposes a new paradigm to increase equity when allocating scarce COVID-19 resources.

Reflections on New Evidence on Crisis Standards of Care in the COVID-19 Pandemic.

Crisis standards of care have been widely developed by healthcare systems and states in the United States during the COVID-19 pandemic, and in some rare cases have actually been used to allocate medical resources. All publicly available U.S. crisis standards of care with a mechanism for allocating scarce resources make use of the Sequential Organ Failure Assessment (SOFA) score in hopes of assigning scarce resources to those patients who are more likely to survive. We reflect on the growing body of evidence suggesting that the SOFA score has limited accuracy in predicting mortality among patients hospitalized with COVID-19 and that the SOFA score systematically disfavors Black patients. Use of the SOFA score for allocating scarce resources may therefore result in Black patients with equal likelihood of survival being deprived of life-saving medical resources. There is also a risk of injustice for patients with non-COVID-19 diagnoses, for whom the SOFA score may be a more accurate prognostic score, but who might nevertheless be unfairly (de)prioritized when assessed alongside COVID-19 patients using the same scoring system. For these reasons we recommend that the SOFA score not be used for triage purposes during the COVID pandemic, and that a national effort be made to develop and empirically test crisis standards of care in advance of the next public health emergency.

Developing a Triage Protocol for the COVID-19 Pandemic: Allocating Scarce Medical Resources in a Public Health Emergency.

The coronavirus disease-2019 (COVID-19) has caused shortages of life-sustaining medical resources, and future waves of the virus may cause further scarcity. The Yale New Haven Health System developed a triage protocol to allocate scarce medical resources during the COVID-19 pandemic, with the primary goal of saving the most lives possible, and a secondary goal of making triage assessments and decisions consistent, transparent, and fair. We outline the process of developing the triage protocol, summarize the protocol itself, and discuss the major ethical challenges encountered, along with our answers to these challenges. These challenges include (1) the role of age and chronic comorbidities; (2) evaluating children and pregnant patients; (3) racial, ethnic, and socioeconomic disparities in health; (4) prioritization of healthcare workers; and (5) balancing clinical judgment versus protocolized assessments. We conclude with a review of the limitations of our protocol and the lessons learned. We hope that a robust public discussion of such protocols and the ethical challenges that they raise will result in the fairest possible processes, less need for triage, and more lives saved during future waves of the COVID-19 pandemic and similar public health emergencies.

A Pot Ignored Boils On: Sustained Calls for Explicit Consent of Intimate Medical Exams.

Unconsented intimate exams (UIEs) on men and women are known to occur for training purposes and diagnostic reasons, mostly during gynecological surgeries but also during prostate examinations and abdominal surgeries. UIEs most often occur on anesthetized patients but have also been reported on conscious patients. Over the last 30 years, several parties-both within and external to medicine-have increasingly voiced opposition to these exams. Arguments from medical associations, legal scholars, ethicists, nurses, and some physicians have not compelled meaningful institutional change. Opposition is escalating in the form of legislative bans and whistleblower reports. Aspiring to professional and scientific detachment, institutional consent policies make no distinction between intimate exams and exams on any other body part, but patients do not think of their intimate regions in a detached or neutral way and believe intimate exams call for special protections. UIEs are found to contribute to moral erosion and moral distress of medical students and compromise the sacred trust between the medical community and the general public. This paper refutes the main arguments in favor of the status quo, identifies a series of harms related to continuing the current practice, and proposes an explicit consent policy for intimate exams along with specific changes to medical school curriculum and institutional culture. Because patients are the rights-holders of their bodies, consent practices should reflect and uphold patient values which call for explicit consent for intimate exams.

Maximizing biomedical research impacts through bioethical considerations.

Bioethics is the formal study of ethical judgments concerning the advances and applications of biology, medicine and related technologies. In a time of unprecedented biomedical advances, it is critical to integrate bioethical frameworks more fully into biomedical research to align these scientific advances with their intended societal needs. In this Perspective, we describe some motivations and frameworks for cross-disciplinary bioethical training for biomedical researchers, and discuss how actively considering bioethics in research and study design could maximize biomedical researchers' intended impacts in society.

New Findings on Unconsented Intimate Exams Suggest Racial Bias and Gender Parity.

Testimony from hundreds of medical students and numerous physicians and scholars suggests that unconsented intimate exams (UIEs) are unlikely to be rare, isolated incidents. However, much is unknown about the frequency of these exams and the circumstances in which they take place. The Community Bioethics Forum, founded and chaired by one of the authors of this commentary, is a consultative group of diverse community members who provide insights on law and policy to policy-makers and medical associations. Connecticut legislators asked the CBF to provide their views on proposed "explicit consent" legislation, and during those discussions, concerning narratives emerged about members' (and their loved ones') personal experiences with UIEs. To gain greater clarity on the demographic patterns and frequency of UIEs, we conducted the first national survey on UIEs. Data from this survey suggest that UIEs may occur under a broader range of circumstances than addressed by most law and policy. The survey resulted in nearly the exact same rate of affirmative responses between males and females in answer to whether they had received a UIE within the past five years. The survey results also showed evidence of racial disparity. Additional research is needed to understand the nature of UIEs.

Social anhedonia is associated with neural abnormalities during face emotion processing.

Human beings are social organisms with an intrinsic desire to seek and participate in social interactions. Social anhedonia is a personality trait characterized by a reduced desire for social affiliation and reduced pleasure derived from interpersonal interactions. Abnormally high levels of social anhedonia prospectively predict the development of schizophrenia and contribute to poorer outcomes for schizophrenia patients. Despite the strong association between social anhedonia and schizophrenia, the neural mechanisms that underlie individual differences in social anhedonia have not been studied and are thus poorly understood. Deficits in face emotion recognition are related to poorer social outcomes in schizophrenia, and it has been suggested that face emotion recognition deficits may be a behavioral marker for schizophrenia liability. In the current study, we used functional magnetic resonance imaging (fMRI) to see whether there are differences in the brain networks underlying basic face emotion processing in a community sample of individuals low vs. high in social anhedonia. We isolated the neural mechanisms related to face emotion processing by comparing face emotion discrimination with four other baseline conditions (identity discrimination of emotional faces, identity discrimination of neutral faces, object discrimination, and pattern discrimination). Results showed a group (high/low social anhedonia) × condition (emotion discrimination/control condition) interaction in the anterior portion of the rostral medial prefrontal cortex, right superior temporal gyrus, and left somatosensory cortex. As predicted, high (relative to low) social anhedonia participants showed less neural activity in face emotion processing regions during emotion discrimination as compared to each control condition. The findings suggest that social anhedonia is associated with abnormalities in networks responsible for basic processes associated with social cognition, and provide a starting point for understanding the neural basis of social motivation and our drive to seek social affiliation.

Unmet Needs, Unwanted Persons: A Call for Expansion of Safe Haven Laws.

Legislative strategies for reducing infant abandonment and neonaticide developed in response to a series of sensational cases that occurred in Texas in 1999. The media coverage of these cases implied that the incidence of the crime was increasing, and Texas legislators responded with a law permitting parents to anonymously surrender their newborn at designated locations such as hospitals. This was the first "safe haven" law. Interest peaked nationwide, and by 2008 all states had a similar version of the law. These laws can trigger rapid cessation of parental rights and a fast-tracked adoption to a preapproved family, reflecting the legislators' assumption that it is better to permit struggling mothers to leave their children with well-intentioned strangers than to abandon the children or end their lives. These laws, however, suffer from several inadequacies.

Distinct associations of insula and cingulate volume with the cognitive and affective dimensions of alexithymia.

Alexithymia ("no words for feelings") is a major risk factor for psychosomatic and psychiatric conditions characterized by affect dysregulation. The alexithymia personality construct comprises an affective dimension, the level of subjective emotional experience (emotionalizing and fantasizing), and a cognitive dimension, referring to the cognitive control of emotions (identifying, analyzing, and verbalizing feelings). These two dimensions may differentially put individuals at risk for psychopathology, but their specific neural bases have rarely been investigated. Therefore, the aim of the present study was to find out whether the two alexithymia dimensions are associated with discriminable neural correlates. By means of voxel-based morphometry (VBM), differences in gray matter volumes were compared between 20 (10 male) high-scorers and 20 (9 male) low-scorers on the Toronto Alexithymia Scale (TAS-20), reflecting the cognitive alexithymia dimension. In a subset of 32 subjects, the impact of the affective alexithymia dimension was tested in addition, as assessed with the affective subscale of the Bermond-Vorst Alexithymia Questionnaire (BVAQ). Analysis 1 (cognitive alexithymia dimension) revealed significantly larger gray matter volumes in the right posterior insula in high-scorers compared to low-scorers on the TAS-20. Analysis 2 (affective alexithymia dimension) revealed that the affective alexithymia dimension, specifically the emotionalizing factor indicative of low emotional reactivity, was associated with larger gray matter volumes of the right cingulate cortex. These results suggest that the two alexithymia dimensions are associated with distinct structural correlates.

The influence of combined cognitive plus social-cognitive training on amygdala response during face emotion recognition in schizophrenia.

Both cognitive and social-cognitive deficits impact functional outcome in schizophrenia. Cognitive remediation studies indicate that targeted cognitive and/or social-cognitive training improves behavioral performance on trained skills. However, the neural effects of training in schizophrenia and their relation to behavioral gains are largely unknown. This study tested whether a 50-h intervention which included both cognitive and social-cognitive training would influence neural mechanisms that support social ccognition. Schizophrenia participants completed a computer-based intervention of either auditory-based cognitive training (AT) plus social-cognition training (SCT) (N=11) or non-specific computer games (CG) (N=11). Assessments included a functional magnetic resonance imaging (fMRI) task of facial emotion recognition, and behavioral measures of cognition, social cognition, and functional outcome. The fMRI results showed the predicted group-by-time interaction. Results were strongest for emotion recognition of happy, surprise and fear: relative to CG participants, AT+SCT participants showed a neural activity increase in bilateral amygdala, right putamen and right medial prefrontal cortex. Across all participants, pre-to-post intervention neural activity increase in these regions predicted behavioral improvement on an independent emotion perception measure (MSCEIT: Perceiving Emotions). Among AT+SCT participants alone, neural activity increase in right amygdala predicted behavioral improvement in emotion perception. The findings indicate that combined cognition and social-cognition training improves neural systems that support social-cognition skills.

Neural activity during emotion recognition after combined cognitive plus social cognitive training in schizophrenia.

Cognitive remediation training has been shown to improve both cognitive and social cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 h (10-week) remediation intervention which included both cognitive and social cognitive training would influence neural function in regions that support social cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 min/day] plus social cognition training (SCT) which was focused on emotion recognition [~5-15 min per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. fMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social cognition training impacts neural mechanisms that support social cognition skills.

Theory of mind skills are related to gray matter volume in the ventromedial prefrontal cortex in schizophrenia.

BACKGROUND: Among individuals with schizophrenia, deficits in theory of mind (ToM) skills predict poor social functioning. Therefore, identifying the neural basis of ToM may assist the development of treatments that improve social outcomes. Despite growing evidence that the ventromedial prefrontal cortex (VMPFC) facilitates ToM skills among healthy individuals, methodological challenges, such as the influence of general cognitive deficits, have made it difficult to identify the relationship between ToM processing and VMPFC function in schizophrenia. METHODS: We used voxel-based morphometry and a multi-method behavioral assessment of ToM processing, including performance-based (Recognition of Faux Pas Test), self-report (Interpersonal Reactivity Index, Perspective-Taking), and interview-rated (Quality of Life Scale-Empathy score) ToM assessments, to investigate whether ToM skills were related to VMPFC gray matter volume (GMV). Standardized neuropsychological measures were used to assess global cognition. Twenty-one schizophrenia and 17 healthy control subjects participated. RESULTS: Between-group behavioral analyses showed that, as compared with healthy participants, schizophrenia participants had worse ToM performance and lower self-reported ToM processing in daily life. The between-group analysis of GMV showed that schizophrenia participants had less VMPFC GMV than healthy participants. Moreover, among schizophrenia participants, all three measures of ToM processing were associated with VMPFC GMV, such that worse ToM skills were related to less VMPFC GMV. This association remained strong for self-reported and interview-rated ToM skills, even when controlling for the influence of global cognition. CONCLUSIONS: The findings suggest that among individuals with schizophrenia, reduced VMPFC GMV is associated with deficits using ToM skills to enhance social relationships.

A multi-classifier and decision fusion framework for robust classification of mammographic masses.

Most end-to-end Computer Aided Diagnosis (CAD) systems follow a three step approach - (1) Image enhancement and segmentation, (2) Feature extraction, and, (3) Classification. While the state of the art in image enhancement and segmentation can now very accurately identify regions of interest for feature extraction, they typically result in very high dimensional feature spaces. This adversely affects the performance of classification systems because a large feature space dimensionality necessitates a large training database to accurately model the statistics of class features (e.g. benign versus malignant classes). In this work, we present a robust multi-classifier decision fusion framework that employs a divide-and-conquer approach for alleviating the affects of high dimensionality of feature vectors. The feature space is partitioned into multiple smaller sized spaces, and a bank of classifiers (a multi-classifier system) is employed to perform classification in each of the partition. Finally, a decision fusion system merges decisions from each classifier in the bank into a single decision. The system is applied to the problem of classifying digital mammographic masses as either benign or malignant.

Digital mammographic computer aided diagnosis (CAD) using adaptive level set segmentation.

We present a mammographic computer aided diagnosis (CAD) system, which uses an adaptive level set segmentation method (ALSSM), which segments suspicious masses in the polar domain and adaptively adjusts the border threshold at each angle to provide high-quality segmentation results. The primary contribution of this paper is the adaptive speed function for controlling level set segmentation. To assess the efficacy of the system, 60 relatively difficult cases (30 benign, 30 malignant) from the Digital Database of Screening Mammography (DDSM) are analyzed. The segmentation efficacy is analyzed qualitatively via visual inspection and quantitatively via the area under the receiver operating characteristics (ROC) curve (AZ) and classification accuracies. For the ALSSM, the best results are 87% overall accuracy, A(Z)=0.9687 with 28/30 malignant cases detected. The qualitative and quantitative results show that the ALSSM provides excellent segmentation and classification results and compares favorably to previous CAD systems in the literature which also used the DDSM database.

Digital mammogram spiculated mass detection and spicule segmentation using level sets.

This letter presents an automated mammographic computer aided diagnosis (CAD) system to detect and segment spicules in digital mammograms, termed spiculation segmentation with level sets (SSLS). SSLS begins with a segmentation of the suspicious mass periphery, which is created using a previously developed adaptive level set segmentation algorithm (ALSSM) by the authors. The mammogram is then analyzed using features derived from the Dixon and Taylor Line Operator (DTLO), which is a method of linear structure enhancement. Features are extracted, optimized, and then the suspicious mass is classified as benign or malignant. To assess the system efficacy, 60 difficult mammographic images from the Digital Database of Screening Mammography (DDSM), containing 30 benign non-spiculated cases, 17 malignant spiculated cases, and 13 malignant non-spiculated cases, are analyzed. The initial spiculation detection method found 100% of the spiculated lesions with no false positive detections, and has area under the receiver operating characteristics (ROC) curve A(Z)=1.0. The values using ALSSM (periphery segmentation only) are A(Z)=0.9687 and 0.9708 for two investigated feature sets, and increases to A(Z)=0.986 2 using SSLS (spiculation segmentation). The best classification results are 93% overall accuracy (OA), with three false positives (FP) and one false negative (FN) using a 1-NN (Nearest Neighbor) or 2-NN classifier, and 92% OA with three FP and two FN using a maximum likelihood classifier.