Evidence map›Paper›PMID 37027067›Full record

ArticleClinical and experimental medicine2023

Five cluster classifications of long COVID and their background factors: A cross-sectional study in Japan.

Tomoya Tsuchida, Naohito Yoshimura, Kosuke Ishizuka, Kohta Katayama, Yoko Inoue, Masanori Hirose, Yu Nakagama, Yasutoshi Kido, Hiroki Sugimori, Takahide Matsuda and 1 more

Abstract read
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Article in Clinical and experimental medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Observational
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Overview of Long COVID: Navigating the Aftermath.Journal of Brown hospital medicine · 2025
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
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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.

Tomoya TsuchidaDepartment of General Internal Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan. tsuchidatomoya@gmail.com.
Naohito YoshimuraDepartment of Nursing, School of Nursing, Iryo Sousei University, Iwaki, Fukushima, Japan.
Kosuke IshizukaDepartment of General Internal Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan.
Kohta KatayamaDepartment of General Internal Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan.
Yoko InoueDepartment of General Internal Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan.
Masanori HiroseDepartment of General Internal Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan.
Yu NakagamaDepartment of Virology & Parasitology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
Yasutoshi KidoDepartment of Virology & Parasitology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
Hiroki SugimoriDepartment of Nursing, School of Sports and Health Sciences, Daito Bunka University, Saitama, Japan.
Takahide MatsudaDepartment of General Internal Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan.
Yoshiyuki OhiraDepartment of General Internal Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae, Kawasaki, Kanagawa, 216-8511, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe long-term symptoms of coronavirus disease 2019 (COVID-19), i.e., long COVID, have drawn research attention. Evaluating its subjective symptoms is difficult, and no established pathophysiology or treatment exists. Although there are several reports of long COVID classifications, there are no reports comparing classifications that include patient characteristics, such as autonomic dysfunction and work status. We aimed to classify patients into clusters based on their subjective symptoms during their first outpatient visit and evaluate their background for these clusters.

methodsIncluded patients visited our outpatient clinic between January 18, 2021, and May 30, 2022. They were aged ≥ 15 years and confirmed to have SARS-CoV-2 infection and residual symptoms lasting at least 2 months post-infection. Patients were evaluated using a 3-point scale for 23 symptoms and classified into five clusters (1. fatigue only; 2. fatigue, dyspnea, chest pain, palpitations, and forgetfulness; 3. fatigue, headache, insomnia, anxiety, motivation loss, low mood, and forgetfulness; 4. hair loss; and 5. taste and smell disorders) using CLUSTER. For continuous variables, each cluster was compared using the Kruskal-Wallis test. Multiple comparison tests were performed using the Dunn's test for significant results. For nominal variables, a Chi-square test was performed; for significant results, a residual analysis was conducted with the adjusted residuals.

resultsCompared to patients in other cluster categories, those in cluster categories 2 and 3 had higher proportions of autonomic nervous system disorders and leaves of absence, respectively.

conclusionsLong COVID cluster classification provided an overall assessment of COVID-19. Different treatment strategies must be used based on physical and psychiatric symptoms and employment factors.

Indexed as

COVID-19Cross-Sectional StudiesFatigueHumansJapanPost-Acute COVID-19 SyndromeSARS-CoV-2Cluster classificationCoronavirus disease 2019Long COVIDSymptoms

Identifiers

PMID37027067
PMCPMC10081305

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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.