Record W4365450166 · doi:10.1038/s41591-023-02274-y

Potential pitfalls in the use of real-world data for studying long COVID

2023· letter· en· W4365450166 on OpenAlexfundno aff

Harrison G. Zhang, Jacqueline Honerlaw, Monika Maripuri, Malarkodi Jebathilagam Samayamuthu, Brendin R. Beaulieu‐Jones, Huma S. Baig, Sehi L’Yi, Yuk‐Lam Ho, Michele Morris, Vidul Ayakulangara Panickan, Xuan Wang, Griffin M. Weber, Katherine P. Liao, Shyam Visweswaran, Bryce W. Q. Tan, William Yuan, Nils Gehlenborg, Sumitra Muralidhar, Rachel Ramoni, James R. Aaron, Giuseppe Agapito, Adem Albayrak, Giuseppe Albi, M Alessiani, Anna Alloni, Danilo F. Amendola, François Angoulvant, Li L.L.J. Anthony, Bruce J. Aronow, Fatima Ashraf, Andrew M. Atz, Paul Avillach, Paula S. Azevedo, James Balshi, Brett K. Beaulieu‐Jones, Douglas S. Bell, Antonio Bellasi, Riccardo Bellazzi, Vincent Benoît, Michele Beraghi, José Luis Bernal-Sobrino, Mélodie Bernaux, Romain Bey, Surbhi Bhatnagar, Alvar Blanco-Martínez, Clara-Lea Bonzel, John Booth, Silvano Bòsari, Florence T. Bourgeois, Robert L. Bradford, Stéphane Breant, Nicholas W. Brown, Raffaele Bruno, William Bryant, Mauro Bucalo, Emily M. Bucholz, Anita Burgun, Tianxi Cai, Mario Cannataro, Aldo Carmona, Charlotte Caucheteux, Julien Champ, Jin Chen, Krista Y. Chen, Luca Chiovato, Lorenzo Chiudinelli, Kelly Cho, James J. Cimino, Tiago K. Colicchio, Sylvie Cormont, Sébastien Cossin, Jean B. Craig, Juan Luis Cruz-Bermúdez, Jaime Cruz‐Rojo, Arianna Dagliati, Mohamad Daniar, Christel Daniel, Priyam Das, Batsal Devkota, Audrey Dionne, Rui Duan, Julien Dubiel, Scott L. DuVall, Loïc Estève, Hossein Estiri, Shirley Fan, Robert W Follett, Thomas Ganslandt, Noelia García Barrio, Lana X. Garmire, Emily Getzen, Alon Geva, Tobias Gradinger, Alexandre Gramfort, Romain Griffier, Nicolas Griffon, Olivier Grisel, Alba Gutiérrez‐Sacristán, Larry Han, David A. Hanauer, Christian Haverkamp, Derek Hazard, Bing He, Darren W. Henderson, Martin Hilka, John H. Holmes, Chuan Hong, Kenneth M. Huling, Meghan R. Hutch, Richard Issitt, Anne‐Sophie Jannot, Vianney Jouhet, Ramakanth Kavuluru, Mark S. Keller, Chris J. Kennedy, Daniel Key, Katie Kirchoff, Jeffrey G. Klann, Ian D. Krantz, Detlef Kraska, Ashok Krishnamurthy, Trang T. Le, Judith Leblanc, Guillaume Lemaître, Leslie Lenert, Damien Leprovost, Molei Liu, Ne Hooi Will Loh, Qi Long, Sara Lozano‐Zahonero, Yuan Luo, Kristine E. Lynch, Sadiqa Mahmood, Sarah E. Maidlow, Adeline Makoudjou, Alberto Malovini, Kenneth D. Mandl, Chengsheng Mao, Anupama Maram, Patricia Martel, Marcelo Roberto Martins, Jayson S. Marwaha, Aaron J. Masino, Maria Mazzitelli, Arthur Mensch, Marianna Milano, Marcos Ferreira Minicucci, Bertrand Moal, Taha Mohseni Ahooyi, Jason H. Moore, Cinta Moraleda, Jeffrey S. Morris, Karyn Moshal, Sajad Mousavi, Danielle L. Mowery, Douglas A. Murad, Shawn N. Murphy, Thomas P. Naughton, Carlos Tadeu Breda Neto, Antoine Neuraz, Jane W. Newburger, Kee Yuan Ngiam, Wanjikũ Njoroge, James B. Norman, Jihad S. Obeid, Marina Politi Okoshi, Karen L. Olson, Gilbert S. Omenn, Nina Orlova, Brian D. Ostasiewski, Nathan Palmer, Nicolás Paris, Lav P. Patel, Miguel Pedrera‐Jiménez, Emily Pfaff, Ashley Pfaff, Danielle Pillion, Sara Pizzimenti, Hans U. Prokosch, Robson Prudente, Andrea Prunotto, Víctor Quirós González, Maryna Raskin, Siegbert Rieg, Gustavo Roig-Domínguez, Pablo Rojo, Paula Rubio-Mayo, Paolo Sacchi, Carlos Sáez, Elisa Salamanca, L. Nelson Sanchez‐Pinto, Arnaud Sandrin, Nandhini Santhanam, Janaina C.C. Santos, Fernando J Sanz Vidorreta, Emily Schriver, Petra Schubert, Juergen Schuettler, Luigia Scudeller, Neil J. Sebire, Pablo Serrano Balazote, Patricia Serre, Arnaud Serret-Larmande, Mohsin Shah, Zahra Shakeri Hossein Abad, Domenick Silvio, Piotr Sliz, Jiyeon Son, Charles Sonday, Andrew M. South, Anastassia Spiridou, Zachary H. Strasser, Amelia L.M. Tan, Byorn W.L. Tan, Suzana Érico Tanni, Deanne M. Taylor, Ana I. Terriza-Torres, Valentina Tibollo, Patric Tippmann, Emma M. S. Toh, Carlo Torti, Enrico Maria Trecarichi, Yi‐Ju Tseng, Andrew K. Vallejos, Gaël Varoquaux, Margaret E. Vella, Guillaume Verdy, Jill-Jênn Vie, Michele Vitacca, Kavishwar B. Wagholikar, Lemuel R. Waitman, Demián Wassermann, Martin Wolkewitz, Scott W. Wong, Xin Xiong, Ye Ye, Nadir Yehya, Alberto Zambelli, Daniela Zöller, Valentina Zuccaro, Chiara Zucco, Isaac S. Kohane, Zongqi Xia, Gabriel A. Brat

Bibliographic record

VenueNature Medicine · 2023

Typeletter

Languageen

FieldMedicine

TopicLong-Term Effects of COVID-19

Canadian institutionsnot available

FundersNorth Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel HillU.S. National Library of MedicineUniversity of PittsburghBritish Heart FoundationNational Institute of Neurological Disorders and StrokePerelman School of Medicine, University of PennsylvaniaUniversitat Politècnica de ValènciaNational Central UniversityNational Center for Advancing Translational SciencesWake Forest School of MedicineMichigan Institute for Clinical and Health ResearchAssistance publique-Hôpitaux de ParisUniversidade Estadual PaulistaNational University of SingaporeChildren's Hospital of PhiladelphiaAlbert-Ludwigs-Universität FreiburgMcGill UniversityMedical Center, University of PittsburghU.S. Department of Veterans AffairsOffice of Research and DevelopmentNorthwestern UniversityUniversity of PennsylvaniaSchool of Public Health, University of MichiganFeinberg School of Medicine

KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakData sciencePolitical scienceVirologyMedicineComputer sciencePathology

DOI10.1038/s41591-023-02274-y

Abstract

fetched live from OpenAlex

No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.214

metaresearch head score (Gemma)0.503

Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated

Candidate categoriesMetaresearch

Consensus categoriesMetaresearch

DomainCandidate signal: Methods · Consensus signal: none

Study designCandidate signal: Not applicable · Consensus signal: Not applicable

GenreCandidate signal: Commentary · Consensus signal: Commentary

Teacher disagreement score0.786

Threshold uncertainty score0.969

Distilled classifier scores by category (both heads)

Category	Codex	Gemma
Metaresearch	0.214	0.503
Meta-epidemiology (narrow)	0.001	0.001
Meta-epidemiology (broad)	0.003	0.002
Bibliometrics	0.004	0.006
Science and technology studies	0.002	0.010
Scholarly communication	0.008	0.012
Open science	0.006	0.005
Research integrity	0.021	0.032
Insufficient payload (model declined to judge)	0.005	0.004

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.

Opus teacher head0.088

GPT teacher head0.391

Teacher spread0.303 · how far apart the two teachers sit on this one work

Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it