A Letter to Our Reviewers— the Core of Pediatric Quality and Safety
Bibliographic record
Abstract
Ten years ago, we embarked on a journey to create a medical journal focused exclusively on quality improvement and patient safety in the pediatric community. Our mission was to create “an international, peer-reviewed, open-access, online periodical dedicated to providing healthcare professionals a forum to disseminate the results of quality improvement and patient safety initiatives that impact the lives of children from fetus to young adulthood.” With our publisher’s support, Wolters Kluwer-LWW, Pediatric Quality and Safety (PQS) was born and, in September 2016, published its first issue. This initial issue featured commentaries from the chief executive officers of 2 children’s hospitals—reflecting their strong support for quality improvement and patient safety and the importance of a journal, such as PQS, to disseminate and publicize that work. Since then, PQS has received 1,246 original manuscripts, published 687 papers, and is now indexed on all major indices. It received its first impact factor (1.1) in June 2023, which increased to 1.2 last year. This result is an outstanding achievement, considering that fewer than 15% of the 30,000 published medical journals receive an impact factor. Although most PQS authors are from the United States, we receive many papers from other regions, including Canada, Europe, Asia, Africa, and Australia. The PQS website receives over 6,000 views each month. Seventy percent of website visitors are from the United States; however, 20% are from the United Kingdom, India, Canada, Australia, and China. Pediatric Quality and Safety would not be possible without the strong support of a pediatric quality improvement and safety community that serves as peer reviewers for the Journal. Each published manuscript has at least 2 peer reviewers with quality improvement and subject matter content expertise. Journal peer reviewing can be a “thankless job,” and while the recognition for the time and effort required to complete a thorough and constructive critique is limited, it is essential for any journal. To this end, we are working with our publishers to develop a CME component for journal reviews, similar to other larger journals such as NEJM and Pediatrics. Much of our Journal’s success has derived from the more than 2,000 PQS peer reviewers and our superb Editorial Board, who have given their time and expertise to help us publish cutting-edge and pragmatic quality improvement work. We applaud your dedication and THANK YOU for your commitment to the core component of our journal—high-quality peer review. Those reviewers who have reviewed 10 or more manuscripts are listed in the table, ordered by the number of completed reviews. - Thomas Bartman, MD, PhD Onsy Ayad, MD Vicki Montgomery, MD David C. Stockwell, MD, MBA Loren Berman, MD, MHS Brendan Boyle, MD Sandra Spencer, MD Raina Paul, MD Jon Wispe, MD, MD Colleen Briana Bertoni, MD, MBOE Brian Joy, MD Kristen M. Crandall, MSN, RN, CPN Ryan Bode, MD Uday Chalwadi, MD Munish Gupta, MD Julie Samora, MD, PhD Amy Louise Billett, MD Anthony Alexander Sochet, MD, MSHS Jayant Deshpande, MD, MPH Vijay Srinivasan, MD Rosalyn Stewart, MD, MS, MBA Gary Frank, MD Elizabeth Mack, MD, MS Lennox Huang, MD Joshua C. Uffman, MD Ryan Coller, MD, MPH Olivia Lund Hoffman, MD Carol Kemper, PhD, RN Jahnavi Valleru, MS, MHA Courtney Nelson, MD Lloyd Provost, PhD Steven Allen, MD Laurel Moyer, MD Rahul Shah, MD, MBA Kelly C. Sandberg, MD, MSc Daniel J. Scherzer, MD Jason Newland, MD, MEd Shannon H. Baumer-Mouradian, MD Kevin J. Little, MD Robert Gajarski, MD Anne Stack, MD Derek Wakeman, MD Sandip Godambe, MD, PhD James Hoffman, PharmD, MS Anthony Lee, MD Christopher Dandoy, MD Anup Patel, MD Stephen Andrew Spooner, MD, MS Todd Karsies, MD Eugenia K Pallotto, MD Tensing Maa, MD Mike Fetzer, BSISE Jeffrey Lutmer, MD Anu Subramony, MD, MD, MBA Michael F. Wells, MD Jamie Macklin, MD Jack Stevens, PhD Shawn Rangel, MD, MSCE Kathleen E. Walsh, MD, MS Daniel Ehrmann, MD, MS Thomas Taghon, DO Jonathan Thackeray, MD Ashley Cooper, MD Gail Bagwell, DNP Roopali Bapat, MD Anthony Sochet, MD, MSHS W. Charles Huskins, MD, MSc Allison King, PharmD Christopher Bonafide, MD, MSCE Joshua Watson, MD Claudia Algaze, MD, MS Robert Angert, MD Elizabeth D. Allen, MD Jennifer Melvin, MD Daniel Hyman, MD Brian Coley, MD Edward G. Shepherd, MD Ryan Breuer, MD Beverly Brozanski, MD Jeffrey B. Anderson, MD, MPH, MBA Lori Rutman, MD, MPH Berkeley Bennett, MD Paul Sharek, MD, MPH Paul C. Mullan, MD, MPH Beth Emerson, MD Trisha Marshall, MD Lamia Soghier, MD Deena Berkowitz, MD, MPH Mark Del Beccaro, MD Janet Berry, DNP, RN, MBA, NEA-BC, CNOR Derek S. Wheeler, MD, MMM, MBA Dmitry Tumin, PhD John Chuo, MD Megan M. Letson, MD, MEd Tetsu (Butch) Uejima, MD Christina L. Cifra, MD, MS William Matthew Linam, MD, MS Peter Lachman, MD, MPH, FRCPCH Thomas J. Caruso, MD John Mahan, MD Ramachandra Bhat, MD Kerry Rosen, MD Leigh Anne Bakel, MD Jacqueline B. Corboy, MD, MS Dane Snyder, MD Theresa R. Grover, MD Anthony Piazza, MD Jeffrey Hoffman, MD Kris Jatana, MD Randal Olshefski, MD Kelly Kelleher, MD, MPH Marlene Miller, MD, MSc
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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".