Evidence map›Paper›PMID 39231657›Full record

ReviewInternal medicine (Tokyo, Japan)2024

Internal Medicine Year in Review 2023.

Naruaki Ogasawara, Kazuto Matsunaga, Hajime Isomoto, Wataru Shimizu

Abstract readReview
In one paragraph

Review in Internal medicine (Tokyo, Japan), 2024. 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
–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

1 citing paper in PubMed.

  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

4 authors.

Naruaki OgasawaraEditorial Department, The Japanese Society of Internal Medicine (JSIM), Japan.
Kazuto MatsunagaDepartment of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Japan.
Hajime IsomotoDepartment of Multidisciplinary Internal Medicine, Tottori University, Japan.
Wataru ShimizuDepartment of Cardiovascular Medicine, Nippon Medical School, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The year 2023 marked a significant change for Internal Medicine, as the number of submissions related to the novel coronavirus infection (COVID-19) declined significantly and interest shifted to other disease fields and research areas. Our journal published its first articles on artificial intelligence (AI) and machine learning (ML), and these articles have shown that AI may be useful for the early detection of potential cardiac diseases, while ML can be used to predict the risk of serious illness in patients hospitalized with COVID-19, providing new possibilities for diagnoses and treatment. In addition to touching on the above, the present article also highlights the status of submissions to the journal (including the number of submissions and acceptance rate) in 2023.

Indexed as

Artificial IntelligenceCOVID-19Internal MedicineHumansMachine LearningPeriodicals as TopicSARS-CoV-2academic informationartificial intelligenceCOVID-19Internal Medicinemachine learningyear in review

Identifiers

PMID39231657
PMCPMC11671193

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.