Bibliographic record
Abstract
The JCRS 100 Club The 100 Club acknowledges those who have reviewed 100 or more manuscripts for the journal. We give our heartfelt thanks for their time and contribution toward making the journal the prestigious publication it is. We could not have done it without them. All members are listed below; those new in 2025 are shown inboldface. Marco Alberti, MD, Milano, Italy Jorge L. Alió, MD, PhD, Alicante, Spain David Allen, FRCOphth, Sunderland, United Kingdom Noel Alpins, MD, Cheltenham, Victoria, Australia Rana Altan-Yaycioglu, MD, FEBO, Adana, Turkey Lisa B. Arbisser, MD, Sarasota, Florida Paul N. Arnold, MD, Ashland, Oregon Steve A. Arshinoff, MD, FRCSC, Toronto, Ontario, Canada Ehud I. Assia, MD, Kfar-Saba, Israel Gordon Balazsi, MD, Mount-Royal, Quebec, Canada James Banta, MD, Miami, Florida George Beiko, MD, St. Catharines, Ontario, Canada Roberto Bellucci, MD, Salò, Italy Hiroko Bissen-Miyajima, MD, PhD, Tokyo, Japan James C. Bobrow, MD, Clayton, Missouri Sandra M. Brown, MD, Concord, North Carolina Jens Buehren, MD, Hanau, Germany Massimo Busin, MD, Forli, Italy David F. Chang, MD, Los Altos, California Soon-Phaik Chee, FRCS(G), FRCOphth(UK), FRCS(Ed), Singapore, Singapore Alan S. Crandall, MD, Salt Lake City, Utah Wayne Crewe-Brown, MBChB, MMed(Ophth), St. Briavels, United Kingdom Daniel G. Dawson, MD, Gainesville, Florida Mohammad Reza Djodeyre, MD, PhD, Zaragoza, Spain William J. Dupps, MD, PhD, Cleveland, Ohio Timo Eppig, PhD, Homburg, Germany Oliver Findl, MD, Vienna, Austria José Luis Güell, MD, Barcelona, Spain David R. Hardten, MD, Minnetonka, Minnesota Ken Hayashi, MD, Fukuoka City, Japan Nino Hirnschall, MD, Vienna, Austria Kenneth J. Hoffer, MD, Santa Monica, California Richard Hoffman, MD, Eugene, Oregon Jack T. Holladay, MD, Bellaire, Texas Farid Karimian, MD, Tehran, Iran Douglas D. Koch, MD, Houston, Texas L. Stephen Kwok, PhD, Sydney, Australia Stephen S. Lane, MD, Stillwater, Minnesota Richard L. Lindstrom, MD, Bloomington, Minnesota Samuel Masket, MD, Los Angeles, California Rupert Menapace, MD, Vienna, Austria Robert Montés-Micó, PhD, Valencia, Spain Jonathan E. Moore, FRCOphth, PhD, Belfast, United Kingdom Majid Moshirfar, MD, Draper, Utah Kristian Næser, MD, Randers, Denmark Mayank A. Nanavaty, MBBS, DO, FRCOphth, PhD, Brighton United Kingdom Thomas F. Neuhann, MD, Munich, Germany Rudy M.M.A. Nuijts, MD, PhD, Maastricht, the Netherlands Thomas Olsen, MD, PhD, Aarhus N, Denmark Randall J. Olson, MD, Salt Lake City, Utah Tetsuro Oshika, MD, Tsukuba, Japan Seth M. Pantanelli, MD, MD, Hershey, Pennsylvania Konrad Pesudovs, PhD, Adelaide, Australia Marcony R. Santhiago, MD, PhD, São Paulo, Brazil Giacomo Savini, MD, Rome, Italy H. John Shammas, MD, Lynwood, California David J. Spalton, FRCP, FRCS, FRCOphth, London, United Kingdom R. Doyle Stulting, MD, PhD, Atlanta, Georgia Rupal H. Trivedi, MD, Charleston, South Carolina Karel Van Keer, MD, PhD, Leuven, Belgium Abhay R. Vasavada, MS, FRCS, Ahmedabad, India Li Wang, MD, PhD, Houston, Texas Theodore P. Werblin, MD, PhD, Princeton, West Virginia Liliana Werner, MD, PhD, Salt Lake City, Utah Diego Zamora-de la Cruz, MD, Ciudad de Mexico, Mexico Charlotta Zetterström, MD, Stockholm, Sweden
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 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 | 0.013 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.315 | 0.308 |
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".