Reviewers for the 2023 IMIA Yearbook of Medical Informatics
Bibliographic record
Abstract
ABEYSINGHE Rashmie, United States AMITH Muhammad, United States ANTONIO Marcy, United States AVALOS Marta, France BAMGBOJE-AYODELE Adeola, Australia BIAN Jiang, United States BITTERMAN Danielle, United States BOYD Andrew, United States BUCKERIDGE David, Canada COLBRAN Laura, United States CORNET Ronald, Netherlands COSSIN Sébastien, France COURTNEY Karen, Canada DANIEL Christel, France DEMETER Naor, Israel DEMIRIS George, United States DENECKE Kerstin, Switzerland DICHMANN SORKNÆS Anne, Denmark DULLABH Prashila, United States DUO Wei, United States ELKIN Peter, United States ELLIS Louise, Australia ESTIRI Hossein, United States FERNÁNDEZ BREIS Jesualdo Tomás, Spain FONG Sarah, United States FREIMUTH Bob, United States GABARRON Elia, Norway GANSLANDT Thomas, Germany GARVIN Jennifer, United States GONG Yang, United States GOODMAN Kenneth, United States GOTTLIEB Assaf, United States GOTTLIEB Laura, United States GRAY Kathleen, Australia HAMON Thierry, France HÄGGLUND Maria, Sweden HASTINGS Janna, United Kingdom HEDERMAN Lucy, Ireland HOLMES John, United States HUANG Zhengxing, China INGENERF Josef, Germany JACKSON Tim, Australia JAIN Sandeep, United States JAMIESON Trevor, Canada JIN Qiao, United States KANNRY Joseph, United States KAUFMAN David, United States KEMPA-LIEHR Andreas, New Zealand KIBBE Warren, United States KLANN Jeffrey, United States KOKKINAKIS Dimitrios, Sweden KOTRONOULAS Grigorios, United Kingdom KOUMAMBA Aimé Patrice, Gabon KUZIEMSKY Craig, Canada LALECI ERTURKMEN Gokce Banu, Turkey LAMY Jean-Baptiste, France LAU Francis, Canada LIN Frank, Australia LISSORGUES Gaëlle, France LUO Gang, United States MADAOUI Nadia, France MALIN Bradley, United States MARTÍNEZ-COSTA Catalina, Spain MCGREEVEY John, United States MEROLLI Mark, Australia MINARD Anne-Lyse, France MOEN Anne, Norway MOEN Hans, Finland MOREY Paul, United States MUÑOZ CARRERO Adolfo, Spain NEVEOL Aurelie, France NIAZKHANI Zahra, Iran NIKIEMA Jean-Noël, Canada OVERGAARD Shauna, United States PAGELER Natalie, United States PAN Eric, United States PANDOLFE Frank, United States PARK Albert, United States PINNA Andrea, France PIRNEJAD Habibollah, Iran PLATT Jody, United States POON Eric, United States RAISARO Jean Louis, Switzerland RANCE Bastien, France RINNER Christoph, Austria RODRÍGUEZ-GONZÁLEZ Alejandro, Spain ROLLER Roland, Germany SEGAL Mark, United States SHACHAK Aviv, Canada SHALOM Erez, Israel SPEIER William, United States STAMM Tanja, Austria TUBBS Colby, United States VANDENBUSSCHE Pierre-Yves, Netherlands VERSPOOR Karin, Australia VIITANEN Johanna, Finland WAEL Alrifai, United States WALTON Nephi, United States WANG Amy, United States WE Duo (Helen), United States WINTER Alfred, Germany XIA Fei, United States YAN Chao, United States ZACK Travis, United States ZHANG Canlin, United States Publication History Article published online: 26 December 2023 © 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/) Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.236 | 0.199 |
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".