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Record W3200956039 · doi:10.1101/2021.09.16.21263704

Real-world serologic responses to Extended-interval and Heterologous COVID-19 mRNA vaccination in Frail Elderly - Interim report from a prospective observational cohort study

2021· preprint· en· W3200956039 on OpenAlexafffundabout
Donald C. Vinh, Jean‐Philippe Gouin, Diana Cruz-Santiago, Michelle Canac‐Marquis, Stéphane Bernier, Florian Bobeuf, Avik Sengupta, Jean-Philippe Brassard, Alyssa Guerra, Robert Dziarmaga, Anna Pérez, Yizhe Sun, Yongbiao Li, Lucie Roussel, Mélanie J. Langelier, Danbing Ke, Corey Arnold, Martin Pelchat, Marc‐André Langlois, Timothy Evans, Xun Zhang, Bruce Mazer

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcGill UniversityUniversity of OttawaMontreal Children's HospitalInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalConcordia UniversityInstitute of Infection and ImmunityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicineVaccinationHeterologousSerologyAntibodyImmunologyVirologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Background The Coronavirus disease 2019 (Covid-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has prompted accelerated vaccines development. Their use was prioritized to protect the most vulnerable, notably, the elderly. Because of fluctuations in vaccine availability, strategies such as delayed second dose and heterologous prime-boost have been employed. The effectiveness of these strategies in the frail elderly are unknown. Methods In this real-world vaccination study, under a government-decreed rationing strategy, elderly adults residing in long-term care facilities, with or without previously-documented SARS-CoV-2 infection, were administered homologous or heterologous mRNA vaccines, with an extended 16-week interval between doses. Clinical data and blood were serially collected during and after this interval period. Sera were tested for SARS-CoV-2-specific IgG antibodies (to trimeric S; RBD; nucleocapsid) by automated chemiluminescent ELISA. Findings After a significant increase 4 weeks post-prime dose, there was a significant decline in anti-RBD and anti-S IgG levels until the boost dose, followed by an increase 4 weeks later. Previously uninfected individuals exhibited lower antibody responses up to 16 weeks post-prime dose, but achieved comparable levels to previously infected counterparts by 4 weeks post-second dose. Individuals primed with BNT162b2 exhibited larger decrease in anti-RBD and anti-S IgG levels with 16-week interval between doses, compared to those who received mRNA-1273. No differences in antibody levels 4 weeks after the second dose were noted between the two vaccines, in either homologous or heterologous combinations. Interpretations These interim results of this ongoing longitudinal study show that, among frail elderly, neither age, sex, nor comorbidity affect antigenicity of mRNA-based COVID vaccines, but previous SARS-CoV-2 infection and type of mRNA vaccine influenced antibody responses when used with a 16-week interval between doses. Homologous/heterologous use of mRNA vaccines was not associated with significant differences in antibody responses 4 weeks following second dose, supporting their interchangeability. Funding This project was supported by funding from the Public Health Agency of Canada, through the Vaccine Surveillance Reference group and the COVID-19 Immunity Task Force (CITF).

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.415
Teacher spread0.310 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

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