Bibliographic record
Abstract
Current Opinion in Infectious Diseases was launched in 1988. It is part of a successful series of review journals whose unique format is designed to provide a systematic and critical assessment of the literature as presented in the many primary journals. The field of infectious diseases is divided into 11 sections that are reviewed once a year. Each section is assigned a Section Editor, a leading authority in the area, who identifies the most important topics at that time. Here we are pleased to introduce the Section Editors for this issue. SECTION EDITORS José G. MontoyaJosé G. MontoyaDr Montoya is originally from Cali, Colombia and completed his medical degree with honors at Universidad del Valle. He trained in Internal Medicine at Tulane University. He completed his fellowship in Infectious Diseases at Stanford University under the mentorship of Dr Jack S. Remington. Dr Montoya is currently Professor of Medicine, Division of Infectious Diseases, at Stanford University School of Medicine: https://med.stanford.edu/profiles/Jose_Montoya. He is the Director of the National Reference Laboratory for Toxoplasmosis in the United States at the Palo Alto Medical Foundation in Palo Alto, California: http://www.pamf.org/Serology/. He is the founder of the Immunocompromised Host Service at Stanford Hospital. He was elected Fellow of the American College of Physicians (FACP) and Fellow of the Infectious Diseases Society of America” (FIDSA) for having achieved professional excellence in Infectious Diseases. His research interests include toxoplasmosis, infection in immunocompromised hosts and chronic unexplained illnesses likely triggered or aggravated by infection. Trish M. PerlTrish M. PerlDr Perl is a Professor in the Departments of Medicine (Infectious Diseases) and Pathology at Johns Hopkins University School of Medicine in Baltimore, Maryland, USA and in the Department of Epidemiology at the Johns Hopkins Bloomberg School of Public Health, USA. She is Senior Epidemiologist for Johns Hopkins Medicine in Baltimore, Maryland and Florida, USA. Dr Perl received her Bachelor of Arts and medical degree from the University of North Carolina at Chapel Hill, USA, and a Master of Science degree from McGill University in Montreal, Canada. She completed an internship, residency, and fellowship in internal medicine at McGill University (Royal Victoria Hospital) in Montreal and a fellowship in infectious diseases and clinical epidemiology at the University of Iowa Hospitals and Clinics in Iowa City, Iowa, USA. She was the hospital epidemiologist of the Johns Hopkins Hospital between 1996 and 2010. She has extensive practical and research experience in the field of healthcare associated infections and resistant organisms and is world renowned for her innovation and research in the field and the use of research knowledge in the healthcare setting. Dr Perl was the 2006 President of the Society of Hospital Epidemiologists of America (SHEA). In the past, she has served on advisory panels for the Institute of Medicine (IOM), the Centers for Disease Control (CDC) and World Health Organization (WHO) and has been a consultant to the National Institutes of Health (NIH) and Agency for Healthcare Research and Quality (ARHQ). An active researcher, Dr Perl has been a principal and co-principal investigator for studies on healthcare associated infections, bioterrorism syndromic surveillance, respiratory infections and antimicrobial resistance for the Centers for Disease Control and Prevention. She has authored or coauthored over 220 peer-reviewed articles. In addition, she has written multiple chapters and contributed to guidelines and policies relevant to healthcare associated infections at the institutional, state and federal level. Dr Perl has been funded by the Centers for Disease Control and Prevention, the Veteran's Affairs Administration and Industry over the years. Her scientific interests encompass emerging diseases and planning in healthcare facilities for their management, syndromic surveillance, respiratory infections, healthcare associated infections including surgical site infections, Clostridium difficile, emerging infection prevention and interventions to prevent, the transmission of epidemiologically significant organisms, and patient and healthcare worker safety. She is committed to training fellows and others with interests in healthcare epidemiology and has begun working in the developing world to help promote the science and discovery in infection prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".