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Record W4296330936 · doi:10.1128/spectrum.01315-22

Correlation of SARS-CoV-2 Viral Neutralizing Antibody Titers with Anti-Spike Antibodies and ACE-2 Inhibition among Vaccinated Individuals

2022· article· en· W4296330936 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicrobiology Spectrum · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsCanadian Blood ServicesUniversity of AlbertaIsland HealthUniversity of British ColumbiaBC Centre for Disease ControlSt. Paul's HospitalMemorial University of Newfoundland
FundersGovernment of Canada
KeywordsAntibodyTiterMedicineVaccinationAntibody titerImmunologyNeutralizing antibodyVirologyDosingConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

Live viral neutralizing antibody titers are an accepted measure of immunity; however, testing procedures are labor-intensive. COVID-19 antibody and angiotensin converting enzyme-2 (ACE-2) levels have been used as surrogates to live viral neutralizing antibody titers; however, validity among vaccinated individuals is unclear. Using samples from 120 two-dose mRNA vaccinees without previous COVID-19, we found that live viral neutralization was correlated with COVID-19 antibody and ACE2 binding levels. When grouping samples by the time interval between vaccination and sample blood collection, samples collected over 158 days after the first vaccine and over 71 days from the second vaccine demonstrated stronger correlation between live viral neutralization titers and both antibody and ACE2 levels, in comparison to those collected earlier.

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.

Full frame distilled prediction

Teacher imitation

Not 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)0.000
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 score0.187
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.306
Teacher spread0.286 · 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