Evidence map›Paper›PMID 38225984›Full record

ArticleJournal of diabetes research2024

Pathology of Ketoacidosis in Emergency of Diabetic Ketoacidosis and Alcoholic Ketoacidosis: A Retrospective Study.

Katsumasa Koyama, Takatoshi Anno, Yukiko Kimura, Fumiko Kawasaki, Kohei Kaku, Koichi Tomoda, Hideaki Kaneto

Open access · goldAbstract read
In one paragraph

Article in Journal of diabetes research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.3field-weighted citation impact, top 22% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
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 1 institution in 1 country.

Katsumasa KoyamaDepartment of General Internal Medicine 1, Kawasaki Medical School, Okayama 700-8505, Japan.
Takatoshi AnnoDepartment of General Internal Medicine 1, Kawasaki Medical School, Okayama 700-8505, Japan.ORCID https://orcid.org/0000-0002-7862-3385
Yukiko KimuraDepartment of General Internal Medicine 1, Kawasaki Medical School, Okayama 700-8505, Japan.
Fumiko KawasakiDepartment of General Internal Medicine 1, Kawasaki Medical School, Okayama 700-8505, Japan.
Kohei KakuDepartment of General Internal Medicine 1, Kawasaki Medical School, Okayama 700-8505, Japan.
Koichi TomodaDepartment of General Internal Medicine 1, Kawasaki Medical School, Okayama 700-8505, Japan.
Hideaki KanetoDepartment of Diabetes, Metabolism and Endocrinology, Kawasaki Medical School, Kurashiki 701-0192, Japan.
Kawasaki Medical School · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study is aimed at examining which factors are useful for the diagnosis and distinction of ketoacidosis. We recruited 21 diabetic ketoacidosis (DKA) and alcoholic ketoacidosis (AKA) patients hospitalized in Kawasaki Medical School General Medical Center from April 2015 to March 2021. Almost all patients in this study were brought to the emergency room in a coma and hospitalized. All patients underwent blood gas aspiration and laboratory tests. We evaluated the difference in diagnosis markers in emergencies between DKA and alcoholic ketoacidosis AKA. Compared to AKA patients, DKA patients had statistically higher values of serum acetoacetic acid and lower values of serum lactate, arterial blood pH, and base excess. In contrast, total ketone bodies,

Indexed as

AcetoacetatesDiabetes MellitusDiabetic KetoacidosisKetosis3-Hydroxybutyric AcidDehydrationHumansRetrospective Studies3-Hydroxybutyric AcidAcetoacetatesacetoacetic acid

Identifiers

PMID38225984
PMCPMC10789514
OpenAlexW4390673567

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.