Evidence map›Paper›PMID 36131845›Full record

ArticleOpen forum infectious diseases2022

Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States.

Lu Meng, Hannah E Fast, Ryan Saelee, Elizabeth Zell, Bhavini Patel Murthy, Neil Chandra Murthy, Peng-Jun Lu, Lauren Shaw, LaTreace Harris, Lynn Gibbs-Scharf and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Open forum infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

11 authors.

Lu MengCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Hannah E FastCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Ryan SaeleeCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Elizabeth ZellCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Bhavini Patel MurthyCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Neil Chandra MurthyCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Peng-Jun LuCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Lauren ShawCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
LaTreace HarrisCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Lynn Gibbs-ScharfCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Terence ChorbaCDC COVID-19 Response Team, US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A tree model identified adults age ≤34 years, Johnson & Johnson primary series recipients, people from racial/ethnic minority groups, residents of nonlarge metro areas, and those living in socially vulnerable communities in the South as less likely to be boosted. These findings can guide clinical/public health outreach toward specific subpopulations.

Indexed as

booster dosecoronavirusCOVID-19COVID-19 vaccination

Identifiers

PMID36131845
PMCPMC9452182

What OpenQuestion holds

Textmetadata
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.