Reviewers for the 2019 IMIA Yearbook of Medical Informatics
Bibliographic record
Abstract
Mervat Abdelhak, USA Julian Alvarez, France Robert Amland, USA Elske Ammenwerth, Austria Avalos Marta, France Cheick Oumar Bagayoko, Mali Panagiotis Bamidis, Greece Melissa Baysari, Australia Tim Benson, United Kingdom Jiang Bian, USA Georgeta Bordea, France Sue Bowman, USA Andrew Boyd, USA Celia Boyer, Switzerland David Buckeridge, Canada Alex Bui, USA John Carrino, USA Pascal Charbonel, France Jonathan H. Chen, USA Rémy Choquet, France Christopher G. Chute, USA Kevin Bretonnel Cohen, USA Carolina Gómez Conejo, Spain Pascal Coorevits, Belgium Theresa Cullen, USA David Darmon, France Hans Demski, Germany Gayo Diallo, France Brian Dixon, USA Alevtina Dubovitskaya, Switzerland Stephany Duda, USA Martin Dugas, Germany Margo Edmunds, USA Frédéric Ehrler, Switzerland Urs Eisenmann, Germany Noémie Elhadad, USA Peter Elkin, USA Peter Embi, USA William Erdley, USA Susan Fenton, USA Xosé M Fernández, France Giacomo Fiumara, Italy Jason Alan Fries, USA Walter Gall, Austria Thomas Ganslandt, Germany Jennifer Garvin, USA Andrew Georgiou, Australia Guido Giunti, Spain Clément Goehrs, France Kenneth Goodman, USA Maria Hägglund, Sweden Thierry Hamon, France Sébastien Harispe, France Ralf Hofestaedt, Germany Shannon Houser, USA Lukas Huber, Austria Josef Ingenerf, Germany Trevor Jamieson, Canada Igor Jurisica, Canada Johanna Kaipio, Finland Jayashree Kalpathy-Cramer, USA David Kaufman, USA Halil Kilicoglu, USA Jeffrey Klann Jefrey, USA Sebastian Köhler, Germany Dimitrios Kokkinakis, Sweden Mayank Kumar, India Craig Kuziemsky, Canada Antoine Lamer, France Paul Landais, France Thomas A. Lasko, USA Nathan Lea, United Kingdom Thierry Lecroq, France Nelly Leon-Chisen, USA Siaw-Teng Liaw, Australia Frank Lin, Australia Christian Lovis, Switzerland Gang Luo, USA Nadia Madaoui, France Bradley A. Malin, USA Romaric Marcilly, France Luis Marco-Ruiz, Norway Mar Marcos, Spain Santiago Martinez, Norway Catalina Costa Martínez, Austria Mark Merolli, Australia Anne Moen, Norway Hans Moen, Finland Pattanasak Mongkolwat, Thailand Shawn Murphy, USA Radha Nagarajan, USA Aurélie Névéol, France Zahra Niazkhani, Iran Stacy O'Connor, USA Casey Overby Taylor, USA Bunyamin Ozaydin, USA Philip R. O. Payne, USA Niels Peek, United Kingdom Mor Peleg, Israel David Pieczkiewicz, USA Andrea Pinna, France Habibollah Pirnejad, Iran Marie-Cecile Ploy, France Morgan Price, Canada Laritza Rodriguez, USA Lipika Samal, USA Neil Sarkar, USA Matthieu Schuers, France Marco Schweitzer, Austria Chaitanya Shivade, USA Hardeep Singh, USA Berglind Smaradottir, Norway Nathalie Souf, France William Speier, USA Olivier Steichen, France Felix Sukums, Tanzania Hugues Talbot, France Xavier Tannier, France Cui Tao, USA Frantz Thiessard, France Ye Tian, USA Umit Topaloglu, USA Pierre-Yves Vandenbussche, The Netherlands Sumithra Velupillai, Sweden Karin Verspoor, Australia Amy Wang, USA Chunhua Weng, USA Alfred Winter, Germany Klaus-Hendrik Wolf, Germany Hua Xu, USA Pierre Zweigenbaum, France
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.230 | 0.200 |
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".