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Parent mental health and family functioning following diagnosis of CHD: a research agenda and recommendations from the Cardiac Neurodevelopmental Outcome Collaborative
- Erica Sood, Amy Jo Lisanti, Sarah E. Woolf-King, Jo Wray, Nadine Kasparian, Emily Jackson, Mary R. Gregory, Keila N. Lopez, Bradley S. Marino, Trent Neely, Amy Randall, Sinai C. Zyblewski, Cheryl L. Brosig
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- Journal:
- Cardiology in the Young / Volume 31 / Issue 6 / June 2021
- Published online by Cambridge University Press:
- 04 June 2021, pp. 900-914
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Diagnosis of CHD substantially affects parent mental health and family functioning, thereby influencing child neurodevelopmental and psychosocial outcomes. Recognition of the need to proactively support parent mental health and family functioning following cardiac diagnosis to promote psychosocial adaptation has increased substantially over recent years. However, significant gaps in knowledge remain and families continue to report critical unmet psychosocial needs. The Parent Mental Health and Family Functioning Working Group of the Cardiac Neurodevelopmental Outcome Collaborative was formed in 2018 through support from an R13 grant from the National Heart, Lung, and Blood Institute to identify significant knowledge gaps related to parent mental health and family functioning, as well as critical questions that must be answered to further knowledge, policy, care, and outcomes. Conceptually driven investigations are needed to identify parent mental health and family functioning factors with the strongest influence on child outcomes, to obtain a deeper understanding of the biomarkers associated with these factors, and to better understand how parent mental health and family functioning influence child outcomes over time. Investigations are also needed to develop, test, and implement sustainable models of mental health screening and assessment, as well as effective interventions to optimise parent mental health and family functioning to promote psychosocial adaptation. The critical questions and investigations outlined in this paper provide a roadmap for future research to close gaps in knowledge, improve care, and promote positive outcomes for families of children with CHD.
Antisaccade error rates and gap effects in psychosis syndromes from bipolar-schizophrenia network for intermediate phenotypes 2 (B-SNIP2)
- Ling-Yu Huang, Brooke S. Jackson, Amanda L. Rodrigue, Carol A. Tamminga, Elliot S. Gershon, Godfrey D. Pearlson, Matcheri S. Keshavan, Sarah S. Keedy, S. Kristian Hill, John A. Sweeney, Brett A. Clementz, Jennifer E. McDowell
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- Journal:
- Psychological Medicine / Volume 52 / Issue 13 / October 2022
- Published online by Cambridge University Press:
- 24 February 2021, pp. 2692-2701
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Background
Antisaccade tasks can be used to index cognitive control processes, e.g. attention, behavioral inhibition, working memory, and goal maintenance in people with brain disorders. Though diagnoses of schizophrenia (SZ), schizoaffective (SAD), and bipolar I with psychosis (BDP) are typically considered to be distinct entities, previous work shows patterns of cognitive deficits differing in degree, rather than in kind, across these syndromes.
MethodsLarge samples of individuals with psychotic disorders were recruited through the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (B-SNIP2) study. Anti- and pro-saccade task performances were evaluated in 189 people with SZ, 185 people with SAD, 96 people with BDP, and 279 healthy comparison participants. Logistic functions were fitted to each group's antisaccade speed-performance tradeoff patterns.
ResultsPsychosis groups had higher antisaccade error rates than the healthy group, with SZ and SAD participants committing 2 times as many errors, and BDP participants committing 1.5 times as many errors. Latencies on correctly performed antisaccade trials in SZ and SAD were longer than in healthy participants, although error trial latencies were preserved. Parameters of speed-performance tradeoff functions indicated that compared to the healthy group, SZ and SAD groups had optimal performance characterized by more errors, as well as less benefit from prolonged response latencies. Prosaccade metrics did not differ between groups.
ConclusionsWith basic prosaccade mechanisms intact, the higher speed-performance tradeoff cost for antisaccade performance in psychosis cases indicates a deficit that is specific to the higher-order cognitive aspects of saccade generation.
Deactivation of SARS-CoV-2 with pulsed-xenon ultraviolet light: Implications for environmental COVID-19 control
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- Sarah E. Simmons, Ricardo Carrion, Jr, Kendra J. Alfson, Hilary M. Staples, Chetan Jinadatha, William R. Jarvis, Priya Sampathkumar, Roy F. Chemaly, Fareed Khawaja, Mark Povroznik, Stephanie Jackson, Keith S. Kaye, Robert M. Rodriguez, Mark A. Stibich
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 42 / Issue 2 / February 2021
- Published online by Cambridge University Press:
- 03 August 2020, pp. 127-130
- Print publication:
- February 2021
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Objectives:
Prolonged survival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on environmental surfaces and personal protective equipment may lead to these surfaces transmitting this pathogen to others. We sought to determine the effectiveness of a pulsed-xenon ultraviolet (PX-UV) disinfection system in reducing the load of SARS-CoV-2 on hard surfaces and N95 respirators.
Methods:Chamber slides and N95 respirator material were directly inoculated with SARS-CoV-2 and were exposed to different durations of PX-UV.
Results:For hard surfaces, disinfection for 1, 2, and 5 minutes resulted in 3.53 log10, >4.54 log10, and >4.12 log10 reductions in viral load, respectively. For N95 respirators, disinfection for 5 minutes resulted in >4.79 log10 reduction in viral load. PX-UV significantly reduced SARS-CoV-2 on hard surfaces and N95 respirators.
Conclusion:With the potential to rapidly disinfectant environmental surfaces and N95 respirators, PX-UV devices are a promising technology to reduce environmental and personal protective equipment bioburden and to enhance both healthcare worker and patient safety by reducing the risk of exposure to SARS-CoV-2.
Patient contact is the main risk factor for vancomycin-resistant Enterococcus contamination of healthcare workers’ gloves and gowns in the intensive care unit
- Sarah S. Jackson, Kerri A. Thom, Laurence S. Magder, Kristen A. Stafford, J. Kristie Johnson, Loren G. Miller, David P. Calfee, Anthony D. Harris, for the CDC Prevention Epicenters Program
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 39 / Issue 9 / September 2018
- Published online by Cambridge University Press:
- 27 July 2018, pp. 1063-1067
- Print publication:
- September 2018
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Objective
To determine which healthcare worker (HCW) roles and patient care activities are associated with acquisition of vancomycin-resistant Enterococcus (VRE) on HCW gloves or gowns after patient care, as a surrogate for transmission to other patients.
DesignProspective cohort study.
SettingMedical and surgical intensive care units at a tertiary-care academic institution.
ParticipantsVRE-colonized patients on Contact Precautions and their HCWs.
MethodsOverall, 94 VRE-colonized patients and 469 HCW–patient interactions were observed. Research staff recorded patient care activities and cultured HCW gloves and gowns for VRE before doffing and exiting patient room.
ResultsVRE were isolated from 71 of 469 HCWs’ gloves or gowns (15%) following patient care. Occupational/physical therapists, patient care technicians, nurses, and physicians were more likely than environmental services workers and other HCWs to have contaminated gloves or gowns. Compared to touching the environment alone, the odds ratio (OR) for VRE contamination associated with touching both the patient (or objects in the immediate vicinity of the patient) and environment was 2.78 (95% confidence interval [CI], 0.99–0.77) and the OR associated with touching only the patient (or objects in the immediate vicinity) was 3.65 (95% CI, 1.17–11.41). Independent risk factors for transmission of VRE to HCWs were touching the patient’s skin (OR, 2.18; 95% CI, 1.15–4.13) and transferring the patient into or out of bed (OR, 2.66; 95% CI, 1.15–6.43).
ConclusionPatient contact is a major risk factor for HCW contamination and subsequent transmission. Interventions should prioritize contact precautions and hand hygiene for HCWs whose activities involve touching the patient.
Electronically Available Comorbid Conditions for Risk Prediction of Healthcare-Associated Clostridium difficile Infection
- Anthony D. Harris, Alyssa N. Sbarra, Surbhi Leekha, Sarah S. Jackson, J. Kristie Johnson, Lisa Pineles, Kerri A. Thom
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 39 / Issue 3 / March 2018
- Published online by Cambridge University Press:
- 05 February 2018, pp. 297-301
- Print publication:
- March 2018
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OBJECTIVE
To analyze whether electronically available comorbid conditions are risk factors for Centers for Disease Control and Prevention (CDC)-defined, hospital-onset Clostridium difficile infection (CDI) after controlling for antibiotic and gastric acid suppression therapy use.
PATIENTSPatients aged ≥18 years admitted to the University of Maryland Medical Center between November 7, 2015, and May 31, 2017.
METHODSComorbid conditions were assessed using the Elixhauser comorbidity index. The Elixhauser comorbidity index and the comorbid condition components were calculated using the International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes extracted from electronic medical records. Bivariate associations between CDI and potential covariates for multivariable regression, including antibiotic use, gastric acid suppression therapy use, as well as comorbid conditions, were estimated using log binomial multivariable regression.
RESULTSAfter controlling for antibiotic use, age, proton-pump inhibitor use, and histamine-blocker use, the Elixhauser comorbidity index was a significant risk factor for predicting CDI. There was an increased risk of 1.26 (95% CI, 1.19–1.32) of having CDI for each additional Elixhauser point added to the total Elixhauser score.
CONCLUSIONSAn increase in Elixhauser score is associated with CDI. Our study and other studies have shown that comorbid conditions are important risk factors for CDI. Electronically available comorbid conditions and scores like the Elixhauser index should be considered for risk-adjustment of CDC CDI rates.
Infect Control Hosp Epidemiol 2018;39:297–301
Guidance on Frequency and Location of Environmental Sampling for Acinetobacter baumannii
- Alyssa N. Sbarra, Anthony D. Harris, J. Kristie Johnson, Laurence S. Madger, Lyndsay M. O’Hara, Sarah S. Jackson, Kerri A. Thom
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 39 / Issue 3 / March 2018
- Published online by Cambridge University Press:
- 30 January 2018, pp. 339-342
- Print publication:
- March 2018
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We assessed various locations and frequency of environmental sampling to maximize information and maintain efficiency when sampling for Acinetobacter baumannii. Although sampling sites in closer proximity to the patient were more likely positive, to fully capture environmental contamination, we found value in sampling all sites and across multiple days.
Infect Control Hosp Epidemiol 2018;39:339–342
The Effect of Adding Comorbidities to Current Centers for Disease Control and Prevention Central-Line–Associated Bloodstream Infection Risk-Adjustment Methodology
- Sarah S. Jackson, Surbhi Leekha, Laurence S. Magder, Lisa Pineles, Deverick J. Anderson, William E. Trick, Keith F. Woeltje, Keith S. Kaye, Kristen Stafford, Kerri Thom, Timothy J. Lowe, Anthony D. Harris
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 38 / Issue 9 / September 2017
- Published online by Cambridge University Press:
- 03 July 2017, pp. 1019-1024
- Print publication:
- September 2017
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BACKGROUND
Risk adjustment is needed to fairly compare central-line–associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes.
METHODSUsing a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank.
RESULTSOverall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51–0.59) for the ICU-type model and 0.64 (95% CI, 0.60–0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model.
CONCLUSIONSOur risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals.
Infect Control Hosp Epidemiol 2017;38:1019–1024
Indirect Versus Direct Standardization Methods for Reporting Healthcare-Associated Infections: An Analysis of Central Line–Associated Bloodstream Infections in Maryland
- Lyndsay M. O’Hara, Max Masnick, Surbhi Leekha, Sarah S. Jackson, Natalia Blanco, Anthony D. Harris
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 38 / Issue 8 / August 2017
- Published online by Cambridge University Press:
- 19 June 2017, pp. 989-992
- Print publication:
- August 2017
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Whether healthcare-associated infection data should be presented using indirect (current CMS/CDC methodology) or direct standardization remains controversial. We applied both methods to central-line–associated bloodstream infection data from 45 acute-care hospitals in Maryland from 2012 to 2014. We found that the 2 methods generate different hospital rankings with payment implications.
Infect Control Hosp Epidemiol 2017;38:989–992
A Simple In-Season Bioassay for Detecting Glyphosate Resistance in Grass and Broadleaf Weeds Prior to Herbicide Application in the Field
- Shiv S. Kaundun, Sarah-Jane Hutchings, Suzanne C. Harris, Lucy V. Jackson, Rekha Shashi-Kiran, Richard P. Dale, Eddie McIndoe
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- Journal:
- Weed Science / Volume 62 / Issue 4 / December 2014
- Published online by Cambridge University Press:
- 20 January 2017, pp. 597-607
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The implementation of a successful glyphosate resistance management strategy requires a simple and cost-effective method for detecting resistance in key weeds. To date, however, glyphosate resistance is still routinely confirmed via laborious and time consuming whole-plant pot assays using seeds collected at the end of the growing season. Here, we describe a simple, early-season bioassay for detecting evolved glyphosate resistance in grass and broadleaf weeds. It involves transplanting suspected glyphosate resistant seedlings alongside known sensitive and resistant standards into agar containing informative rates of herbicide and recording percentage survival 14 d after plating. The method was validated using sensitive and resistant populations of Lolium, Eleusine, Conyza, and Amaranthus species encompassing the main glyphosate resistance mechanisms, namely, impaired translocation, EPSPS gene duplication, and mutations. The whole plant pot and agar-based seedling tests generated comparable resistance indices in dose-response assays and percentage survival at discriminating glyphosate rates. The method was applied successfully to detect resistance in a rigid ryegrass population collected from a French vineyard well before glyphosate was applied in the field for the current season. Additionally, the test was shown to be highly transferable to several other grass and broadleaf weeds that have evolved resistance to glyphosate. One major attribute of the method is that it is capable of detecting resistance regardless of the mechanism involved. In addition to being very simple, quick and, cost-effective, it allows determination of glyphosate resistance in weeds prior to field application. It thus offers the opportunity for an informed choice of herbicides for effective weed control.
Improving Risk Adjustment Above Current Centers for Disease Control and Prevention Methodology Using Electronically Available Comorbid Conditions
- Sarah S. Jackson, Surbhi Leekha, Lisa Pineles, Laurence S. Magder, Kerri A. Thom, Yuan Wang, Anthony D. Harris
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 37 / Issue 10 / October 2016
- Published online by Cambridge University Press:
- 15 July 2016, pp. 1173-1178
- Print publication:
- October 2016
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OBJECTIVE
To identify comorbid conditions associated with surgical site infection (SSI) among patients undergoing renal transplantation and improve existing risk adjustment methodology used by the Centers for Disease Control and Prevention National Healthcare Safety Network (NHSN).
PATIENTSPatients (≥18 years) who underwent renal transplantation at University of Maryland Medical Center January 1, 2010-December 31, 2011.
METHODSTrained infection preventionists reviewed medical records to identify surgical site infections that developed within 30 days after transplantation, using NHSN criteria. Patient demographic characteristics and risk factors for surgical site infections were identified through a central data repository. International Statistical Classification of Disease, Ninth Revision, Clinical Modification codes were used to analyze individual component comorbid conditions and calculate the Charlson and Elixhauser comorbidity indices. These indices were compared with the current NHSN risk adjustment methodology.
RESULTSA total of 441 patients were included in the final cohort. In bivariate analysis, the Charlson components of cerebrovascular disease, peripheral vascular disease, and rheumatologic disorders and Elixhauser components of obesity, rheumatoid arthritis, and weight loss were significantly associated with the outcome. A model utilizing the variables from the NHSN methodology had a c-statistic of 0.56 (95% CI, 0.48–0.63), whereas a model that also included comorbidities from the Charlson and Elixhauser indices had a c-statistic of 0.65 (95% CI, 0.58–0.73). The model with all 3 risk adjustment scores performed best and was statistically different from the NHSN model alone, demonstrated by improvement in the c statistic (0.65 vs 0.56).
CONCLUSIONRisk adjustment models should incorporate electronically available comorbid conditions.
Infect Control Hosp Epidemiol 2016;1–6
Pseudomonas aeruginosa Colonization in the Intensive Care Unit: Prevalence, Risk Factors, and Clinical Outcomes
- Anthony D. Harris, Sarah S. Jackson, Gwen Robinson, Lisa Pineles, Surbhi Leekha, Kerri A. Thom, Yuan Wang, Michelle Doll, Melinda M. Pettigrew, J. Kristie Johnson
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 37 / Issue 5 / May 2016
- Published online by Cambridge University Press:
- 01 February 2016, pp. 544-548
- Print publication:
- May 2016
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OBJECTIVE
To determine the prevalence of Pseudomonas aeruginosa colonization on intensive care unit (ICU) admission, risk factors for P. aeruginosa colonization, and the incidence of subsequent clinical culture with P. aeruginosa among those colonized and not colonized.
METHODSWe conducted a cohort study of patients admitted to a medical or surgical intensive care unit of a tertiary care hospital. Patients had admission perirectal surveillance cultures performed. Risk factors analyzed included comorbidities at admission, age, sex, antibiotics received during current hospitalization before ICU admission, and type of ICU.
RESULTSOf 1,840 patients, 213 (11.6%) were colonized with P. aeruginosa on ICU admission. Significant risk factors in the multivariable analysis for colonization were age (odds ratio, 1.02 [95% CI, 1.01–1.03]), anemia (1.90 [1.05–3.42]), and neurologic disorder (1.80 [1.27–2.54]). Of the 213 patients colonized with P. aeruginosa on admission, 41 (19.2%) had a subsequent clinical culture positive for P. aeruginosa on ICU admission and 60 (28.2%) had a subsequent clinical culture positive for P. aeruginosa in the current hospitalization (ICU period and post-ICU period). Of these 60 patients, 49 (81.7%) had clinical infections. Of the 1,627 patients not colonized on admission, only 68 (4.2%) had a subsequent clinical culture positive for P. aeruginosa in the current hospitalization. Patients colonized with P. aeruginosa were more likely to have a subsequent positive clinical culture than patients not colonized (incidence rate ratio, 6.74 [95% CI, 4.91–9.25]).
CONCLUSIONSPrediction rules or rapid diagnostic testing will help clinicians more appropriately choose empirical antibiotic therapy for subsequent infections.
Infect Control Hosp Epidemiol 2016;37:544–548
Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. Lemos, Ernest LePore, Sarah-Jane Leslie, Isaac Levi, Andrew Levine, Alan E. Lewis, Daniel E. Little, Shu-hsien Liu, Shu-hsien Liu, Alan K. L. Chan, Brian Loar, Lawrence B. Lombard, John Longeway, Dominic McIver Lopes, Michael J. Loux, E. J. Lowe, Steven Luper, Eugene C. Luschei, William G. Lycan, David Lyons, David Macarthur, Danielle Macbeth, Scott MacDonald, Jacob L. Mackey, Louis H. Mackey, Penelope Mackie, Edward H. Madden, Penelope Maddy, G. B. Madison, Bernd Magnus, Pekka Mäkelä, Rudolf A. Makkreel, David Manley, William E. Mann (W.E.M.), Vladimir Marchenkov, Peter Markie, Jean-Pierre Marquis, Ausonio Marras, Mike W. Martin, A. P. Martinich, William L. McBride, David McCabe, Storrs McCall, Hugh J. McCann, Robert N. McCauley, John J. McDermott, Sarah McGrath, Ralph McInerny, Daniel J. McKaughan, Thomas McKay, Michael McKinsey, Brian P. McLaughlin, Ernan McMullin, Anthonie Meijers, Jack W. Meiland, William Jason Melanson, Alfred R. Mele, Joseph R. Mendola, Christopher Menzel, Michael J. Meyer, Christian B. Miller, David W. Miller, Peter Millican, Robert N. Minor, Phillip Mitsis, James A. Montmarquet, Michael S. Moore, Tim Moore, Benjamin Morison, Donald R. Morrison, Stephen J. Morse, Paul K. Moser, Alexander P. D. Mourelatos, Ian Mueller, James Bernard Murphy, Mark C. Murphy, Steven Nadler, Jan Narveson, Alan Nelson, Jerome Neu, Samuel Newlands, Kai Nielsen, Ilkka Niiniluoto, Carlos G. Noreña, Calvin G. Normore, David Fate Norton, Nikolaj Nottelmann, Donald Nute, David S. Oderberg, Steve Odin, Michael O’Rourke, Willard G. Oxtoby, Heinz Paetzold, George S. Pappas, Anthony J. Parel, Lydia Patton, R. P. Peerenboom, Francis Jeffry Pelletier, Adriaan T. Peperzak, Derk Pereboom, Jaroslav Peregrin, Glen Pettigrove, Philip Pettit, Edmund L. Pincoffs, Andrew Pinsent, Robert B. Pippin, Alvin Plantinga, Louis P. Pojman, Richard H. Popkin, John F. Post, Carl J. Posy, William J. Prior, Richard Purtill, Michael Quante, Philip L. Quinn, Philip L. Quinn, Elizabeth S. Radcliffe, Diana Raffman, Gerard Raulet, Stephen L. Read, Andrews Reath, Andrew Reisner, Nicholas Rescher, Henry S. Richardson, Robert C. Richardson, Thomas Ricketts, Wayne D. Riggs, Mark Roberts, Robert C. Roberts, Luke Robinson, Alexander Rosenberg, Gary Rosenkranz, Bernice Glatzer Rosenthal, Adina L. Roskies, William L. Rowe, T. M. Rudavsky, Michael Ruse, Bruce Russell, Lilly-Marlene Russow, Dan Ryder, R. M. Sainsbury, Joseph Salerno, Nathan Salmon, Wesley C. Salmon, Constantine Sandis, David H. Sanford, Marco Santambrogio, David Sapire, Ruth A. Saunders, Geoffrey Sayre-McCord, Charles Sayward, James P. Scanlan, Richard Schacht, Tamar Schapiro, Frederick F. Schmitt, Jerome B. Schneewind, Calvin O. Schrag, Alan D. Schrift, George F. Schumm, Jean-Loup Seban, David N. Sedley, Kenneth Seeskin, Krister Segerberg, Charlene Haddock Seigfried, Dennis M. Senchuk, James F. Sennett, William Lad Sessions, Stewart Shapiro, Tommie Shelby, Donald W. Sherburne, Christopher Shields, Roger A. Shiner, Sydney Shoemaker, Robert K. Shope, Kwong-loi Shun, Wilfried Sieg, A. John Simmons, Robert L. Simon, Marcus G. Singer, Georgette Sinkler, Walter Sinnott-Armstrong, Matti T. Sintonen, Lawrence Sklar, Brian Skyrms, Robert C. Sleigh, Michael Anthony Slote, Hans Sluga, Barry Smith, Michael Smith, Robin Smith, Robert Sokolowski, Robert C. Solomon, Marta Soniewicka, Philip Soper, Ernest Sosa, Nicholas Southwood, Paul Vincent Spade, T. L. S. Sprigge, Eric O. Springsted, George J. Stack, Rebecca Stangl, Jason Stanley, Florian Steinberger, Sören Stenlund, Christopher Stephens, James P. Sterba, Josef Stern, Matthias Steup, M. A. Stewart, Leopold Stubenberg, Edith Dudley Sulla, Frederick Suppe, Jere Paul Surber, David George Sussman, Sigrún Svavarsdóttir, Zeno G. Swijtink, Richard Swinburne, Charles C. Taliaferro, Robert B. Talisse, John Tasioulas, Paul Teller, Larry S. Temkin, Mark Textor, H. S. Thayer, Peter Thielke, Alan Thomas, Amie L. 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