Bibliographic record
Abstract
The Editorial Board thanks the following scientific referees for contributing their time and expertise to review manuscripts submitted to the JCRO from October 2022 through September 2023. Claudette Abela-Formanek, MD, Vienna, Austria Pradeep Agarwal, MS, Moradabad, India Sunita Agarwal, DO, MS, FRSH, Bangalore, India Rana Altan-Yaycioglu, MD, FEBO, Adana, Turkey Renato Ambrosio, MD, PhD, Rio de Janeiro, Brazil Michael Amon, MD, Vienna, Austria Steve A. Arshinoff, MD, FRCSC, Toronto, Ontario, Canada Ehud I. Assia, MD, Kfar-Saba, Israel Bradley Barnett, MD, PhD, Sacramento, California Roberto Bellucci, MD, Salò, Italy Rafael Bilbao-Calabuig, MD, Madrid, Spain Mark H. Blecher, MD, Philadelphia, Pennsylvania Massimo Busin, MD, Forli, Italy Francesco Carones, MD, Milan, Italy Durval M. Carvalho Jr, MD, Brasília, Brazil Armando S. Crema, MD, Rio de Janeiro, Brazil Mohammad Reza Djodeyre, MD, PhD, Zaragoza, Spain Ian J. Dooley, MB BAO BCh MSc MRCOPHTH MRCSI, Dublin, United Kingdom William J. Dupps Jr, MD, PhD, Cleveland, Ohio Allen O. Eghrari, MD, Baltimore, Maryland Luis Fernández-Vega-Cueto, MD, PhD, Oviedo, Spain Nicole Renee Fram, MD, Los Angeles, California Felix Gonzalez-Lopez, MD, Madrid, Spain Christina N. Grupcheva, MD, PhD, DSc, Varna, Bulgaria Richard Yudi Hida, MD, São Paulo, Brazil Nino Hirnschall, MD, Vienna, Austria Stephen B. Kaye, MD, Liverpool, United Kingdom Guy Kleinmann, MD, Holon, Israel Douglas D. Koch, MD, Houston, Texas Florian Kretz, MD, Ahaus, Germany Tracy Krick, MD, Baltimore, Maryland Gilles Lesieur, MD, Albi, France Jeffrey Ma, MD, Sacramento, California Kevin Ma, MD, Boston, Massachusetts Antonio Marinho, MD, Porto, Portugal Samuel Masket, MD, Los Angeles, California, Artemis Matsou, MD, FEBO, MRCP (UK), East Grinstead, United Kingdom Colm McAlinden, MD, PhD, FEBO, FRCOphth, London, United Kingdom Pedro Menéres, MD, Porto, Portugal Rashmi Mittal, MS, Gurgaon, India Majid Moshirfar, MD, Draper, Utah Ewa Mrukwa-Kominek, MD, PhD, FEBO-CR, Silesia, Poland William G. Myers, MD, Chicago, Illinois Mayank Ambarish Nanavaty, MBBS, DO, FRCOphth, Brighton, United Kingdom Julio Ortega-Usobiaga, MD, PhD, FEBOS-CR, Bilbao, Spain Ernesto Otero, MD, Bogota Colombia Seth M. Pantanelli, MD, Hershey, Pennsylvania M.Ceu Brochado Pinto, MD, Gaia, Portugal Arturo Ramirez-Miranda, MD, Mexico City, Mexico Steven G. Safran, MD, Lawrenceville, New Jersey Julie M. Schallhorn, MD, San Francisco, California Richard Schulze, Jr, MPhil(Oxon), MD, Savannah, Georgia Sunil Shah, MD, Solihull, United Kingdom David J. Spalton, FRCP, FRCS, FRCOphth, London, United Kingdom Stephen A. Stewart, MA, FRCOphth, Belfast, United Kingdom R. Doyle Stulting, MD, PhD, Atlanta, Georgia Michael Sulewski, MD, Baltimore, Maryland Audrey R. Talley Rostov, MD, Seattle, Washington John Vukich, MD, Madison, Wisconsin Sarah B. Weissbart, MD, Smithtown, New York Theodore P. Werblin, MD, PhD, Princeton, West Virginia Liliana Werner, MD, PhD, Salt Lake City, Utah Sidra Zafar, MD, Baltimore, Maryland Roger Zaldivar, MD, Mendoza, Argentina Diego Zamora-de la Cruz, MD, Ciudad de Mexico, Mexico
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.321 | 0.237 |
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 source (direct Gemma or distilled Codex), 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".