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BACKGROUND: Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar transmissions of biases. RESEARCH QUESTION: Can we identify implicit bias in clinical notes, and are biases stable across time and geography? STUDY DESIGN AND METHODS: To determine whether different racial and ethnic descriptors are similar contextually to stigmatizing language in ICU notes and whether these relationships are stable across time and geography, we identified notes on critically ill adults admitted to the University of California, San Francisco (UCSF), from 2012 through 2022 and to Beth Israel Deaconess Hospital (BIDMC) from 2001 through 2012. Because word meaning is derived largely from context, we trained unsupervised word-embedding algorithms to measure the similarity (cosine similarity) quantitatively of the context between a racial or ethnic descriptor (eg, African-American) and a stigmatizing target word (eg, nonco-operative) or group of words (violence, passivity, noncompliance, nonadherence). RESULTS: In UCSF notes, Black descriptors were less likely to be similar contextually to violent words compared with White descriptors. Contrastingly, in BIDMC notes, Black descriptors were more likely to be similar contextually to violent words compared with White descriptors. The UCSF data set also showed that Black descriptors were more similar contextually to passivity and noncompliance words compared with Latinx descriptors. INTERPRETATION: Implicit bias is identifiable in ICU notes. Racial and ethnic group descriptors carry different contextual relationships to stigmatizing words, depending on when and where notes were written. Because NLP models seem able to transmit implicit bias from training data, use of NLP algorithms in clinical prediction could reinforce disparities. Active debiasing strategies may be necessary to achieve algorithmic fairness when using language models in clinical research.

ObjectivesThe purpose of this study was to evaluate the impact of pharmacist-driven naloxone training of resident physicians as part of discharge prescribing from an inpatient psychiatric unit.MethodsThis is a prospective pilot study in which psychiatric resident physicians (N = 21) were educated on naloxone administration, prescribing, and counseling. A ten-question survey was designed and delivered immediately pre- and post-training to assess resident knowledge of and comfort with naloxone prescribing. Respondents were asked to rate ten statements on a scale from 1 to 5, with 1 corresponding to "strongly disagree" and 5 corresponding to "strongly agree." The primary objective was to evaluate the impact of training on prescriber knowledge and attitudes using the designed questionnaire. The secondary objective was to assess the difference in naloxone prescribing pre- and post-training implementation. Descriptive statistics and paired t-tests were conducted to assess statistical significance.ResultsPrior to training, 11 resident physicians (approximately 50%) agreed or strongly agreed that they felt knowledgeable about naloxone and approximately 70% (n = 15) felt confident identifying patients who would benefit from naloxone at discharge. Only 10% of resident physicians (n = 2) felt comfortable counseling patients on and administering naloxone during an overdose pre-training compared to 100% after training. Thirty-seven patients were discharged and counseled on naloxone use during the study period.ConclusionPharmacist-driven naloxone training significantly increased physician knowledge and comfort prescribing naloxone and resulted in an increase in naloxone prescriptions upon discharge from an inpatient psychiatric unit.

Disorders of consciousness are neurological conditions characterized by impaired arousal and awareness of self and environment. Behavioural responses are absent or are present but fluctuate. Disorders of consciousness are commonly encountered as a consequence of both acute and chronic brain injuries, yet reliable epidemiological estimates would require inclusive, operational definitions of the concept, as well as wider knowledge dissemination among involved professionals. Whereas several manifestations have been described, including coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state, a comprehensive neurobiological definition for disorders of consciousness is still lacking. The scientific literature is primarily observational, and studies-specific aetiologies lead to disorders of consciousness. Despite advances in these disease-related forms, there remains uncertainty about whether disorders of consciousness are a disease-agnostic unitary entity with a common mechanism, prognosis or treatment response paradigm. Our knowledge of disorders of consciousness has also been hampered by heterogeneity of study designs, variables, and outcomes, leading to results that are not comparable for evidence synthesis. The different backgrounds of professionals caring for patients with disorders of consciousness and the different goals at different stages of care could partly explain this variability. The Prospective Studies working group of the Neurocritical Care Society Curing Coma Campaign was established to create a platform for observational studies and future clinical trials on disorders of consciousness and coma across the continuum of care. In this narrative review, the author panel presents limitations of prior observational clinical research and outlines practical considerations for future investigations. A narrative review format was selected to ensure that the full breadth of study design considerations could be addressed and to facilitate a future consensus-based statement (e.g. via a modified Delphi) and series of recommendations. The panel convened weekly online meetings from October 2021 to December 2022. Research considerations addressed the nosographic status of disorders of consciousness, case ascertainment and verification, selection of dependent variables, choice of covariates and measurement and analysis of outcomes and covariates, aiming to promote more homogeneous designs and practices in future observational studies. The goal of this review is to inform a broad community of professionals with different backgrounds and clinical interests to address the methodological challenges imposed by the transition of care from acute to chronic stages and to streamline data gathering for patients with disorders of consciousness. A coordinated effort will be a key to allow reliable observational data synthesis and epidemiological estimates and ultimately inform condition-modifying clinical trials.

OBJECTIVES: To develop proof-of-concept algorithms using alternative approaches to capture provider sentiment in ICU notes. DESIGN: Retrospective observational cohort study. SETTING: The Multiparameter Intelligent Monitoring of Intensive Care III (MIMIC-III) and the University of California, San Francisco (UCSF) deidentified notes databases. PATIENTS: Adult (≥18 yr old) patients admitted to the ICU. MEASUREMENTS AND MAIN RESULTS: We developed two sentiment models: 1) a keywords-based approach using a consensus-based clinical sentiment lexicon comprised of 72 positive and 103 negative phrases, including negations and 2) a Decoding-enhanced Bidirectional Encoder Representations from Transformers with disentangled attention-v3-based deep learning model (keywords-independent) trained on clinical sentiment labels. We applied the models to 198,944 notes across 52,997 ICU admissions in the MIMIC-III database. Analyses were replicated on an external sample of patients admitted to a UCSF ICU from 2018 to 2019. We also labeled sentiment in 1,493 note fragments and compared the predictive accuracy of our tools to three popular sentiment classifiers. Clinical sentiment terms were found in 99% of patient visits across 88% of notes. Our two sentiment tools were substantially more predictive (Spearman correlations of 0.62-0.84, p values < 0.00001) of labeled sentiment compared with general language algorithms (0.28-0.46). CONCLUSION: Our exploratory healthcare-specific sentiment models can more accurately detect positivity and negativity in clinical notes compared with general sentiment tools not designed for clinical usage.

Purpose: Automatic grouping of data according to certain characteristics is made possible by clustering algorithms, which makes them an essential tool when working with large datasets. However, although they are unsupervised tools, they generally require the specification of the number of clusters to be formed, k, a task that may be simple for a human, but quite complex to automate. Despite the most commonly used k-value selection techniques offer acceptable results, they are not without shortcomings, suggesting that there is ample room for improvement. This paper briefly introduces clustering techniques, discusses the main shortcomings of conventional k-value selection techniques and examines the advantages and limitations of nine promising alternatives presented in recent years. Design/methodology/approach: An evaluation of the main shortcomings of classic k-value estimation techniques has been carried out, and the newest proposals have been explained and compared. Findings: New k-value estimation indices and methodologies proposed by authors guarantee better results, extending the use of these techniques to large volumes of data, and complex shapes and structures. However, no generical methodology able to overcome all the described shortcomings has still been developed. Research limitations/implications: This research is limited to the newest developed techniques for k-value estimation, including proposals published since 2019. Older proposals have not been considered, as the newest ones overcome the former’s shortcomings. A k-value estimation techniques review published in 2019 is cited in the test as a base reference. Practical implications: Although the examples listed in the text apply to industry, the techniques described and discussed in this review are applicable to any area of science that can benefit from the use of clustering techniques. Originality/value: To date, there has been no paper comparing the new k-value estimation techniques. Although there are literature reviews comparing the classical methods, these methods are nowadays nearly obsolete due to the complexity of the data usually faced.

El desarrollo emocional de los estudiantes es un aspecto crucial en su proceso educativo que impacta directamente su rendimiento académico. Este estudio tuvo como objetivo determinar la relación entre el desarrollo emocional y el logro académico en estudiantes universitarios. Se utilizó un método hipotético-deductivo con un diseño no experimental correlacional. Participaron 100 estudiantes, a quienes se les aplicó un cuestionario con una confiabilidad de 0.853 y se registraron sus notas para evaluar el logro académico. Los resultados, basados en la correlación de Spearman de 0.788 con una significancia de 0.000 (menor a 0.05), indican que existe una relación directa entre el desarrollo emocional y el logro académico. Esto sugiere que los esfuerzos para mejorar el desarrollo emocional de los estudiantes podrían ser beneficiosos para su desempeño académico.

Abstract Background Endometriosis, defined as the presence of endometrial-like tissue outside of the uterus, is one of the most prevalent gynecological disorders. Although different theories have been proposed, its pathogenesis is not clear. Novel studies indicate that the gut microbiome may be involved in the etiology of endometriosis; nevertheless, the connection between microbes, their dysbiosis, and the development of endometriosis is understudied. This case–control study analyzed the gut microbiome in women with and without endometriosis to identify microbial targets involved in the disease. Methods A subsample of 1000 women from the Estonian Microbiome cohort, including 136 women with endometriosis and 864 control women, was analyzed. Microbial composition was determined by shotgun metagenomics and microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Partitioning Around Medoids (PAM) algorithm was performed to cluster the microbial profile of the Estonian population. The alpha- and beta-diversity and differential abundance analyses were performed to assess the gut microbiome (species and KEGG orthologies (KO)) in both groups. Metagenomic reads were mapped to estrobolome-related enzymes’ sequences to study potential microbiome-estrogen metabolism axis alterations in endometriosis. Results Diversity analyses did not detect significant differences between women with and without endometriosis (alpha-diversity: all p-values > 0.05; beta-diversity: PERMANOVA, both R 2 0.05). No differential species or pathways were detected after multiple testing adjustment (all FDR p-values > 0.05). Sensitivity analysis excluding women at menopause (> 50 years) confirmed our results. Estrobolome-associated enzymes’ sequence reads were not significantly different between groups (all FDR p-values > 0.05). Conclusions Our findings do not provide enough evidence to support the existence of a gut microbiome-dependent mechanism directly implicated in the pathogenesis of endometriosis. To the best of our knowledge, this is the largest metagenome study on endometriosis conducted to date.

Abstract Miombo woodlands are integral to livelihoods across southern Africa, biodiversity in the region, and the global carbon cycle, making accurate and precise monitoring of their state and change essential. Here, we assembled a terrestrial and airborne lidar dataset covering 50 kha of intact and degraded miombo woodlands, and generated aboveground biomass estimates with low uncertainty via direct 3D measurements of forest structure. We found 1.71 ± 0.09 TgC was stored in aboveground biomass across this landscape, between 1.5 and 2.2 times more than the 0.79–1.14 TgC estimated by conventional methods. This difference is in part owing to the systematic underestimation of large trees by allometry. If these results were extrapolated across Africa’s miombo woodlands, their carbon stock would potentially require an upward revision of approximately 3.7 PgC, implying we currently underestimate their carbon sequestration and emissions potential, and disincentivise their protection and restoration.

Abstract Mitotic errors generate micronuclei entrapping mis-segregated chromosomes, which are susceptible to catastrophic fragmentation through chromothripsis. The reassembly of fragmented chromosomes by error-prone DNA double-strand break (DSB) repair generates diverse genomic rearrangements associated with human diseases. How specific repair pathways recognize and process these lesions remains poorly understood. Here we use CRISPR/Cas9 to systematically inactivate distinct DSB repair pathways and interrogate the rearrangement landscape of fragmented chromosomes. Deletion of canonical non-homologous end joining (NHEJ) components substantially reduces complex rearrangements and shifts the rearrangement landscape toward simple alterations without the characteristic patterns of chromothripsis. Following reincorporation into the nucleus, fragmented chromosomes localize within sub-nuclear micronuclei bodies (MN bodies) and undergo ligation by NHEJ within a single cell cycle. In the absence of NHEJ, chromosome fragments are rarely engaged by alternative end-joining or recombination-based mechanisms, resulting in delayed repair kinetics, persistent 53BP1-labeled MN bodies, and cell cycle arrest. Thus, we provide evidence supporting NHEJ as the exclusive DSB repair pathway generating complex rearrangements from mitotic errors.

O objetivo desta pesquisa foi analisar o impacto da orientação motivacional, da oportunidade percebida e da probabilidade de comportamento desonesto sobre a desonestidade acadêmica de estudantes da área de negócios nos exames on-line durante o Ensino Remoto Emergencial (ERE). Trata-se de uma pesquisa com enfoque predominantemente quantitativo, operacionalizada pela aplicação de um questionário (180 estudantes de uma universidade federal brasileira) e analisada por meio de Modelagem de Equações Estruturais, método dos Mínimos Quadrados Parciais. Adicionalmente, foi realizado o Discurso do Sujeito Coletivo (DSC) para coletar a percepção dos principais motivos da “cola” ser mais frequentemente realizada no período de estudos on-line. Identificou-se que a colaboração coletiva não autorizada, a “cola” e o plágio foram as atitudes antiéticas mais recorrentes no período, sob a justificativa de obtenção de uma nota mais alta. O DSC apontou a falta de supervisão e de punição, além das facilidades de comunicação no período on-line, para justificar o fato de a “cola” ser mais presente no ERE do que no ensino presencial. O modelo estrutural revelou (i) uma relação negativa entre orientação motivacional e desonestidade acadêmica e (ii) uma relação positiva entre oportunidade percebida e probabilidade de comportamento desonesto sobre a desonestidade acadêmica. O estudo fornece subsídios empíricos da percepção dos alunos nos exames on-line, oferecendo uma explicação potencial para a postura (anti)ética e sua frequência, além de uma proxy para a compreensão do comportamento universitário nesse contexto.