Evidence map›Paper›PMID 37275271›Full record

ArticleVaccine: X2023

Establishment of prediction equations for subcutaneous tissue thickness in two representative intramuscular deltoid injections.

Ryosuke Kowatari, Hanae Sasaki, Kenyu Murata, Ken Sato, Koichi Sagawa, Masako Kudo, Masahito Minakawa

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Vaccine: X, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 30% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

7 authors at 2 institutions in 1 country.

Ryosuke KowatariDepartment of Thoracic and Cardiovascular Surgery, Hirosaki University School of Medicine, Japan.
Hanae SasakiDepartment of Thoracic and Cardiovascular Surgery, Hirosaki University School of Medicine, Japan.
Kenyu MurataDepartment of Thoracic and Cardiovascular Surgery, Hirosaki University School of Medicine, Japan.
Ken SatoHirosaki University Health Administration Center, Japan.
Koichi SagawaHirosaki University Graduate School of Science and Technology, Japan.
Masako KudoHirosaki University Hospital Nursing Department, Japan.
Masahito MinakawaDepartment of Thoracic and Cardiovascular Surgery, Hirosaki University School of Medicine, Japan.
Hirosaki University · JPHirosaki University Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study clarifies the predicted subcutaneous shoulder depth and investigates the safety of the conventional (three-finger breadth method) and new (axillary method) intramuscular injection methods. The anatomical features of 245 volunteers who received the COVID-19 vaccination via the conventional method were investigated at the injection site (T point) and the hypothetical injection site using the new method (A point) via ultrasonography. The body mass index (BMI) and subcutaneous thickness at the T point (men: r = 0.75; women: r = 0.45) and the A point (men: r = 0.81; women: r = 0.55) were positively correlated. The upper arm circumference and subcutaneous thickness at the T point (r = 0.51) and the A point (r = 0.58) were correlated in women. Formulas to predict subcutaneous thickness using BMI and upper arm circumference were established: predicted subcutaneous thickness at the A point = 0.62 × BMI - 7.7 mm (R

Indexed as

COVID-19Intramuscular injectionShoulder injuries related to vaccine administratioVaccination

Identifiers

PMID37275271
PMCPMC10239006
OpenAlexW4377287451

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.