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Record W4297440859 · doi:10.1136/rmdopen-2022-002622

Third dose corrects waning immunity to SARS-CoV-2 mRNA vaccines in immunocompromised patients with immune-mediated inflammatory diseases

2022· article· en· W4297440859 on OpenAlexafffundabout
Michelle W. Cheung, Roya Monica Dayam, Jaclyn C. Law, Rogier Goetgebuer, Gary Chao, Naomi Finkelstein, Joanne M. Stempak, Daniel Pereira, David Croitoru, Lily Acheampong, Saima Rizwan, Jenny D. Lee, Darshini Ganatra, Ryan Law, Melanie Delgado-Brand, Geneviève Mailhot, Vincent Piguet, Mark S. Silverberg, Tania H. Watts, Anne‐Claude Gingras, Vinod Chandran

Bibliographic record

VenueRMD Open · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity Health NetworkLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineImmune systemImmunityImmunologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakVirologySars virusInfectious disease (medical specialty)DiseasePathology

Abstract

fetched live from OpenAlex

Funded by Canadian Institutes of Health (CIHR)/COVID-Immunity Task Force (CITF) grants VR-1 172 711 and vs1-175545 (to THW and ACG), CIHR FDN-143250 (to THW), GA2-177716 (to VC, A-CG and THW) and GA1-177703 (to ACG) and the CIHR rapid response network to SARS-CoV-2 variants, CoVaRR-Net (to ACG) as well as a donation from Juan and Stefania Speck.

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 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.027
GPT teacher head0.319
Teacher spread0.292 · 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

Classification

machine, unvalidated

Machine 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 designObservational
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

Citations3
Published2022
Admission routes3
Has abstractyes

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