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Human genetic and immunological determinants of critical COVID-19 pneumonia
Qian Zhang, Paul Bastard, Adem Karbuz, Adrian Gervais, Ahmad Abou Tayoun, Alessandro Aiuti, Alexandre Bélot, Alexandre Bolze, Alexandre Gaudet, Анастасія Бондаренко, Zhiyong Liu, András N. Spaan, Andrea Guennoun, Andrés A. Arias, Anna M. Planas, Anna Šedivá, Anna Shcherbina, Anna‐Lena Neehus, Anne Puel, Antoine Froidure, Antonio Novelli, Aslınur Özkaya Parlakay, Aurora Pujol, Aysun Yahşi, Belgi̇n Gülhan, Benedetta Bigio, Bertrand Boisson, Beth A. Drolet, Carlos A. Arango-Franco, Carlos Flores, Carlos Rodríguez‐Gallego, Carolina Prando, Catherine M. Biggs, Charles‐Édouard Luyt, Clifton L. Dalgard, Cliona O’Farrelly, Daniela Matuozzo, David Dalmau, David S. Perlin, Davood Mansouri, Diederik van de Beek, Donald C. Vinh, Elena Domínguez‐Garrido, Elena W.Y. Hsieh, Emine Hafize Erdeniz, Emmanuelle Jouanguy, Esra Şevketoğlu, Estelle Talouarn, Eugenia Quirós-Roldán, Evangelos Andreakos, Eystein S. Husebye, Fahad Alsohime, Filomeen Haerynck, Giorgio Casari, Giuseppe Novelli, Gökhan Aytekіn, Guillaume Morelle, Gülsüm Alkan, Gülsüm İclal Bayhan, Hagit Baris Feldman, Helen C. Su, Horst von Bernuth, Igor Resnick, Ingrid G. Bustos, Isabelle Meyts, Isabelle Migeotte, Ivan Tancevski, Jacinta Bustamante, Jacques Fellay, Jamila El Baghdadi, Javier Martínez‐Picado, Jean‐Laurent Casanova, Jérémie Rosain, Jérémy Manry, Jie Chen, John Christodoulou, Jonathan Bohlen, José Luis Franco, Juan Li, Juan‐Manuel Anaya, Julián Rojas, YE Junqiang, K. M. Furkan Uddin, Kadriye Kart Yaşar, Kai Kisand, Keisuke Okamoto, Khalil Chaïbi, Kristina Mironska, László Maródi, Laurent Abel, Laurent Rénia, Lazaro Lorenzo, Lennart Hammarström, Lisa F. P. Ng, Lluís Quintana‐Murci, Lucía Victoria Erazo-Borrás, Luigi D. Notarangelo, Luis Felipe Reyes, Luís M. Allende, Luisa Imberti, Majistor Raj Luxman Maglorius Renkilaraj, Marcela Moncada‐Vélez, Marie Materna, Mark S. Anderson, Marwa Chbihi, Masato Ogishi, Melike Emiroğlu, Mikko Seppänen, Mohammed Uddin, Mohammed Shahrooei, Natalie Alexander, Nevin Hatipoğlu, Nico Marr, Nihal Akçay, Oksana Boyarchuk, Ondřej Slabý, Özge Metin Akcan, Peng Zhang, Pere Soler‐Palacín, Peter K. Gregersen, Petter Brodin, Pierre Garçon, Pierre‐Emmanuel Morange, Qiang Pan‐Hammarström, Qinhua Zhou, Quentin Philippot, Rabih Halwani, Rebeca Pérez de Diego, Romain Lévy, Rui Yang, Şadiye Kübra Tüter Öz, Saleh Al Muhsen, Saliha Kanık Yüksek, Sara Elva Espinosa‐Padilla, Sathishkumar Ramaswamy, Satoshi Okada, Şefika Elmas Bozdemir, Selma Erol Aytekin, Şemsi Nur Karabela, Sevgi Keleş, Sevtap Şenoğlu, Shen‐Ying Zhang, Sotiriјa Duvlis, Stefan N. Constantinescu, Stéphanie Boisson‐Dupuis, Stuart E. Turvey, Stuart G. Tangye, Takaki Asano, Tayfun Özçelık, Tom Le Voyer, Tom Maniatis, Tomohiro Morio, Trine H. Mogensen, Vanessa Sancho‐Shimizu, Vivien Béziat, Xavier Solanich, Yenan T. Bryceson, YL Lau, Yuval Itan, Aurélie Cobat
Bibliographic record
VenueNature · 2022
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsBC Children's HospitalMcGill University Health CentreHospital for Sick ChildrenSt. Paul's HospitalUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthHoward Hughes Medical InstituteAgence Nationale de la RechercheNational Human Genome Research Institute
KeywordsPneumoniaImmunologyTLR7DiseaseImmunityMedicineViral pneumoniaAtypical pneumoniaImmune systemAntibodyBiologyCoronavirus disease 2019 (COVID-19)Innate immune systemInternal medicineInfectious disease (medical specialty)Toll-like receptor
Abstract
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 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.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 head0.150
GPT teacher head0.519
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview
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".
Quick stats
Citations391
Published2022
Admission routes1
Has abstractno