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Delayed production of neutralizing antibodies correlates with fatal COVID-19
Carolina Lucas, Jon Klein, Maria E. Sundaram, Feimei Liu, Patrick Wong, Julio Silva, Tianyang Mao, Ji Eun Oh, Subhasis Mohanty, Jiefang Huang, Maria Tokuyama, Peiwen Lu, Arvind Venkataraman, Annsea Park, Benjamin Israelow, Chantal B. F. Vogels, M. Catherine Muenker, C‐Hong Chang, Arnau Casanovas‐Massana, Adam J. Moore, Joseph Zell, John Fournier, Abeer Obaid, Alexander J. Robertson, Alice Lu-Culligan, Alice Zhao, Allison Nelson, Anderson F. Brito, Ángela Núñez, Anjelica Martin, Anne E. Watkins, Bertie Geng, Caitlin J. Chun, Chaney C. Kalinich, Christina A. Harden, Codruta Todeasa, Cole Jensen, Coriann E. Dorgay, Daniel Kim, David McDonald, Denise Shepard, Edward Courchaine, Elizabeth B. White, Eric Song, Erin Silva, Eriko Kudo, Giuseppe DeIuliis, Harold Rahming, Hong‐Jai Park, Irene Matos, Isabel M. Ott, Jessica Nouws, Jordan Valdez, Joseph R. Fauver, Joseph K. Lim, Kadi-Ann Rose, Kelly Anastasio, Kristina Brower, Laura Glick, Lokesh Sharma, Lorenzo R. Sewanan, Lynda Knaggs, Maksym Minasyan, Maria Batsu, Mary E. Petrone, Maxine Kuang, Maura Nakahata, Melissa Linehan, Michael H. Askenase, Michael Simonov, Mikhail Smolgovsky, Natasha C. Balkcom, Nicole Sonnert, Nida Naushad, Pavithra Vijayakumar, Rick Martinello, Rupak Datta, Ryan Handoko, Santos Bermejo, Sarah Prophet, Sean Bickerton, Sofia Velazquez, Tara Alpert, Tyler Rice, William Khoury-Hanold, Xiaohua Peng, Yexin Yang, Yiyun Cao, Yvette Strong, Zitong Lin, Anne L. Wyllie, Melissa Campbell, Alfred Ian Lee, Hyung J. Chun, Nathan D. Grubaugh, Wade L. Schulz, Shelli Farhadian, Charles S. Dela Cruz, Aaron M. Ring, Albert C. Shaw, Adam V. Wisnewski, İnci Yıldırım, Albert I. Ko, Saad B. Omer, Akiko Iwasaki
Bibliographic record
VenueNature Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Institute for Occupational Safety and HealthNational Institute on AgingNational Center for Advancing Translational SciencesYale School of Public Health, Yale UniversityNederlandse Organisatie voor Wetenschappelijk OnderzoekGruber FoundationYale UniversityNational Science FoundationU.S. Department of Health and Human ServicesPew Charitable TrustsG. Harold and Leila Y. Mathers FoundationU.S. Department of Veterans AffairsLudwig Family FoundationNational Institute of Allergy and Infectious DiseasesHoward Hughes Medical Institute
KeywordsCoronavirus disease 2019 (COVID-19)VirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Antibody2019-20 coronavirus outbreakNeutralizing antibodyBetacoronavirusCoronavirus InfectionsBiologyMedicineImmunologyOutbreakDiseaseInternal medicineInfectious disease (medical specialty)
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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.547
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 head0.030
GPT teacher head0.366
Teacher spread0.336 · 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.
The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical
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
Citations263
Published2021
Admission routes1
Has abstractno