Evidence map›Paper›PMID 40262279›Full record

ArticleJMIR formative research2025

CoVimmune COVID-19 Immunity Calculator: Web Application Development and Validation Study.

Rebecca Slotkin, Tassos C Kyriakides, Vinni Yu, Xien Chen, Anupam Kundu, Shaili Gupta

Abstract readValidation Study
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Rebecca SlotkinDepartment of Medicine, Yale School of Medicine, 950 Campbell Ave, Bldg 1, Floor 5, West Haven, CT, 06516, United States, 1 2038005320.ORCID 0000-0002-4469-7191
Tassos C KyriakidesCooperative Studies Program Coordinating Center, VA CT Healthcare System, West Haven, CT, 06516, United States.ORCID 0000-0001-8913-108X
Vinni YuDepartment of Computer Science, Yale University, New Haven, CT, United States.ORCID 0009-0007-5051-4607
Xien ChenDepartment of Computer Science, Yale University, New Haven, CT, United States.ORCID 0009-0000-7397-4955
Anupam KunduYale Center for Analytical Sciences, Yale School of Public Health, New Haven, CT, United States.ORCID 0000-0003-3542-9280
Shaili GuptaDepartment of Medicine, Yale School of Medicine, 950 Campbell Ave, Bldg 1, Floor 5, West Haven, CT, 06516, United States, 1 2038005320.ORCID 0000-0001-9508-0995

Funding

TRAINING PROGRAM IN INVESTIGATIVE RHEUMATOLOGYT32AR007107 · NIAMS · YALE UNIVERSITY · PI RICHARD J BUCALA · 1986 to 2026
$7.5M
NIAMS NIH HHS T32 AR007107
6 · The paper itself

Abstract

Background: This study illustrates the development of a simple web-based application, which demonstrates the relationship between serum anti-SARS-CoV-2 S1/receptor-binding domain immunoglobulin G (IgG) and anti-SARS-CoV-2 neutralizing antibody (nAb) half-maximal inhibitory concentration (IC50) titers in a vaccinated US adult population and compares them to prior data on nAb titers at different time points after vaccination. Objective: The objective of this study is to create an easily accessible calculator that uses the results of commercially available anti-SARS-CoV-2 serum IgG to approximate the underlying ability to neutralize SARS-CoV-2. Methods: Our web-based application leveraged two previously published datasets. One dataset demonstrated a robust correlation between nAb and serum IgG. The other dataset measured nAb titers at specific time periods over a year-long interval following a messenger RNA vaccination primary series and booster vaccine dose. Clinical factors that were statistically significant on a forward linear regression model examining the prediction of nAb from serum IgG were incorporated in the application tool. Results: By combining the datasets described above, we developed a publicly available web-based application that allows users to enter a serum IgG value and determine their estimated nAb titer. The application contextualizes the estimated nAb titer with the theoretical distance from the corresponding vaccine-mediated antibody protection. Using the clinical variables that had a significant impact on how well IgG values predict nAb titers, this application allows for a patient-centered, nAb titer prediction. Conclusions: This application offers an example of how we might bring the advances made in scientific research on protective antibodies post-SARS-CoV-2 vaccination into the clinical sphere with practical tools.

Indexed as

Antibodies, NeutralizingAntibodies, ViralCOVID-19COVID-19 VaccinesImmunoglobulin GSARS-CoV-2AdultFemaleHumansInternetMaleMiddle AgedAntibodies, NeutralizingAntibodies, ViralCOVID-19 VaccinesImmunoglobulin GCOVID-19immunityimmunoglobulin Gneutralizing antibodypatient-centered careSARS-CoV-2vaccinationvaccine hesitancyvaccine timingweb application

Identifiers

PMID40262279
PMCPMC12040297

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.