Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Immunocompromised patients with acute respiratory distress syndrome: Secondary analysis of the LUNG SAFE database

Background: The aim of this study was to describe data on epidemiology, ventilatory management, and outcome of acute respiratory distress syndrome (ARDS) in immunocompromised patients. Methods: We performed a post hoc analysis on the cohort of immunocompromised patients enrolled in the Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE) study. The LUNG SAFE study was an international, prospective study including hypoxemic patients in 459 ICUs from 50 countries across 5 continents. Results: Of 2813 patients with ARDS, 584 (20.8%) were immunocompromised, 38.9% of whom had an unspecified cause. Pneumonia, nonpulmonary sepsis, and noncardiogenic shock were their most common risk factors for ARDS. Hospital mortality was higher in immunocompromised than in immunocompetent patients (52.4% vs 36.2%; p &lt; 0.0001), despite similar severity of ARDS. Decisions regarding limiting life-sustaining measures were significantly more frequent in immunocompromised patients (27.1% vs 18.6%; p &lt; 0.0001). Use of noninvasive ventilation (NIV) as first-line treatment was higher in immunocompromised patients (20.9% vs 15.9%; p = 0.0048), and immunodeficiency remained independently associated with the use of NIV after adjustment for confounders. Forty-eight percent of the patients treated with NIV were intubated, and their mortality was not different from that of the patients invasively ventilated ab initio. Conclusions: Immunosuppression is frequent in patients with ARDS, and infections are the main risk factors for ARDS in these immunocompromised patients. Their management differs from that of immunocompetent patients, particularly the greater use of NIV as first-line ventilation strategy. Compared with immunocompetent subjects, they have higher mortality regardless of ARDS severity as well as a higher frequency of limitation of life-sustaining measures. Nonetheless, nearly half of these patients survive to hospital discharge. Trial registration: ClinicalTrials.gov, NCT02010073. Registered on 12 December 2013

Aportes para la Gestión Intersectorial

Fil: Juárez, A. Secretaría de Ambiente y Desarrollo Sustentable; Argentina.Fil: Segura, M. Secretaría de Ambiente y Desarrollo Sustentable; Argentina.Fil: González, A. Secretaría de Provincias; Argentina.Fil: Verrastro, O. Secretaría de Provincias; Argentina.Fil: Abraham, J. Secretaría de Política Económica; Argentina.Fil: Clot, M. Secretaría de Política Económica; Argentina.Fil: Sardi, R.I. Secretaría de Política Económica; Argentina.Fil: Asato, C.G. Servicio Geológico Minero Argentino; Argentina.Fil: Lapido, O. Servicio Geológico Minero Argentino; Argentina.Fil: Dall´Armellina, M. Subsecretaría de Planificación Territorial de la Inversión Pública; Argentina.Fil: Pomposiello, G. Subsecretaría de Planificación Territorial de la Inversión Pública; Argentina.Fil: Puricelli, G. Subsecretaría de Planificación Territorial de la Inversión Pública; Argentina.Fil: Romero, M.E. Subsecretaría de Planificación Territorial de la Inversión Pública; Argentina.Fil: Seiguer, H. Subsecretaría de Planificación Territorial de la Inversión Pública; Argentina.Fil: Abraham, M. Subsecretaría de Agricultura, Ganadería y Forestación; Argentina.Fil: Berthelot, M. Secretaría de Turismo; Argentina.Fil: Corral, A. Secretaría de Turismo; Argentina.Fil: Gallardo, C. Secretaría de Turismo; Argentina.Fil: Pelliza, V. Secretaría de Turismo; Argentina.Fil: Roberti, N. Secretaría de Turismo; Argentina.Fil: Rolón, C. Secretaría de Turismo; Argentina.Fil: Salomone, L. Secretaría de Turismo; Argentina.Fil: Aristimuño, A. Secretaría de Ambiente y Desarrollo Sustentable; Argentina.Fil: di Loreto, M. Secretaría de Ambiente y Desarrollo Sustentable; Argentina.Fil: Ortíz, Y. Secretaría de Ambiente y Desarrollo Sustentable; Argentina.Fil: Reichembach, A. Secretaría de Ambiente y Desarrollo Sustentable; Argentina.Fil: Villariño, R. Secretaría de Ambiente y Desarrollo Sustentable; Argentina.Fil: Franzese, G. Secretaría de Provincias; Argentina.Fil: Weich, M. Secretaría de Política Económica; Argentina.Fil: Longueira, S. Secretaría de Energía; Argentina.Fil: Pino, F. Secretaría de Energía; Argentina.Fil: Chalabe, N. Secretaría de Políticas Sociales y Desarrollo Humano; Argentina.Fil: Bermúdez, O. Secretaría de Ciencia, Tecnología e Innovación Productiva; Argentina.Fil: Grané, M. Secretaría de Empleo; Argentina.Fil: Carllinni, J. Subsecretaría de Gestión Pública; Argentina.Fil: Cerezo, M. Subsecretaría de Gestión Pública; Argentina.Fil: Pozzi, I. Subsecretaría de Gestión Pública; Argentina.Fil: Begenisic, F. Subsecretaría de Agricultura, Ganadería y Forestación; Argentina.Fil: Pascale, C. Subsecretaría de Agricultura, Ganadería y Forestación; Argentina.Fil: Foce, S. Subsecretaría de Desarrollo Urbano y Vivienda; Argentina.Fil: Rodríguez, E. Subsecretaría de Desarrollo Urbano y Vivienda; Argentina.Fil: Jovanovich, O. Subsecretaría de Planificación Territorial de la Inversión Pública; Argentina.Fil: Valente, G. Subsecretaría de Planificación Territorial de la Inversión Pública; Argentina.Fil: Marzioni, G. Subsecretaría de Tierras para el Hábitat Social; Argentina.Fil: Juliá, M. Subsecretaría de Recursos Hídricos; Argentina.Fil: Clot, M. Subsecretaría de Transporte Ferroviario; Argentina.Fil: Roccatagliata, J. Subsecretaría de Transporte Ferroviario; Argentina.Fil: Saller, V.H. Subsecretaría de Transporte Ferroviario; Argentina.Fil: Altuve, S. Instituto Nacional de Tecnología Agropecuaria (INTA); Argentina.Fil: Carrapizo, V. Instituto Nacional de Tecnología Agropecuaria (INTA); Argentina.Fil: Ligier, D. Instituto Nacional de Tecnología Agropecuaria (INTA); Argentina.Fil: Saucede, M. Instituto Nacional de Tecnología Agropecuaria (INTA); Argentina.Fil: Burkart, R. Administración de Parques Nacionales; Argentina.Fil: Bluske, G. Instituto Nacional de Estadísticas y Censos (INDEC); Argentina.Fil: Taddei, N. Instituto Nacional de Estadísticas y Censos (INDEC); Argentina.Fil: Vibes, J. Instituto Nacional de Estadísticas y Censos (INDEC); Argentina.Fil: Álvarez, P. Comisión Nacional de Actividades Espaciales; Argentina.Fil: Hernández, A.M. Comisión Nacional de Actividades Espaciales; Argentina.Fil: Goulart, H. Comisión Nacional de Energía Atómica (CNEA); Argentina.Fil: Oñate, M.S. Comisión Nacional de Energía Atómica (CNEA); Argentina

Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Background: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life. Methods: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed. Results: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth. Conclusions: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy

Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders

Importance Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood. Objective To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. Design, Setting, and Participants Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244. Main Outcomes and Measures Interregional profiles of group difference in cortical thickness between cases and controls. Results A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders. Conclusions and Relevance In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders