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Impact of SARS-CoV-2 infection and COVID-19 on patients with inborn errors of immunity
Stuart G. Tangye, Laurent Abel, Salah Al-Muhsen, Alessandro Aiuti, Saleh Al‐Muhsen, Fahd Al‐Mulla, Mark S. Anderson, Evangelos Andreakos, Antonio Novelli, Andrés A. Arias, Hagit Baris Feldman, Alexandre Bélot, Catherine M. Biggs, Ahmed Aziz Bousfiha, Petter Brodin, John Christodoulou, Antônio Condino‐Neto, Clifton L. Dalgard, Sara Elva Espinosa‐Padilla, Jacques Fellay, Carlos Flores, José Luis Franco, Antoine Froidure, Filomeen Haerynck, Rabih Halwani, Lennart Hammarström, Sarah E. Henrickson, Elena W.Y. Hsieh, Yuval Itan, Timokratis Karamitros, YL Lau, Davood Mansouri, Isabelle Meyts, Trine H. Mogensen, Tomohiro Morio, Lisa F. P. Ng, Luigi D. Notarangelo, Giuseppe Novelli, Satoshi Okada, Tayfun Özçelık, Qiang Pan‐Hammarström, Rebeca Pérez de Diego, Carolina Prando, Aurora Pujol, Laurent Rénia, Igor Resnick, Carlos Rodríguez‐Gallego, Vanessa Sancho‐Shimizu, Mikko Seppänen, Anna Shcherbina, Andrew L. Snow, Pere Soler‐Palacín, András N. Spaan, Ivan Tancevski, Ahmad Abou Tayoun, Şehime Gülsün Temel, Stuart E. Turvey, Mohammed Uddin, Donald C. Vinh, Mayana Zatz, Keisuke Okamoto, David S. Pelin, Graziano Pesole, Diederik van de Beek, Roger Colobrán, Joost Wauters, Helen C. Su, Jean‐Laurent Casanova
Bibliographic record
VenueJournal of Allergy and Clinical Immunology · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIINational Center for Advancing Translational SciencesMedical Research CouncilMohammed Bin Rashid University of Medicine and Health SciencesUniversität InnsbruckBilkent ÜniversitesiHospital for Sick ChildrenUniversity of New South WalesNHLBI Division of Intramural ResearchAgency for Science, Technology and ResearchNational Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesFisher Center for Alzheimer's Research FoundationJeffrey Modell FoundationAgence Nationale de la RechercheMedizinische Universität InnsbruckEuropean CommissionHelsingin YliopistoBursa Uludağ ÜniversitesiLee Kong Chian School of Medicine, Nanyang Technological UniversityMinistère de l'Enseignement supérieur, de la Recherche et de l'InnovationUniformed Services University of the Health SciencesNovo Nordisk FondenUniversiteit van AmsterdamBC Children's HospitalTokyo Medical and Dental UniversityAarhus UniversitetFondation pour la Recherche MédicaleKU LeuvenNational Health and Medical Research CouncilUniversidade de São PauloHackensack Meridian HealthInstitució Catalana de Recerca i Estudis AvançatsFondation du SouffleMcGill UniversityNational Institutes of HealthMcGill University Health CentreAmsterdam NeuroscienceKarolinska InstitutetInstitut National de la Santé et de la Recherche MédicaleImperial College LondonUniversity of British Columbia
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyImmunityBetacoronavirusMedicineImmunologyOutbreakImmune systemInfectious disease (medical specialty)DiseaseInternal medicine
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.070
GPT teacher head0.392
Teacher spread0.322 · 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 teacher head, not a consensus.
Study designOther design
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
Citations41
Published2022
Admission routes1
Has abstractno