Evidence map›Paper›PMID 34006322›Full record

ArticleJournal of medical case reports2021

Elective laparoscopic colectomy in a patient 3 weeks after coronavirus disease 2019 infection: a case report.

Yuki Tateno, Kimito Harada, Fumiki Okamoto, Hideo Katsuragawa

Open access · goldAbstract readCase Reports
In one paragraph

Article in Journal of medical case reports, 2021. 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
0.2field-weighted citation impact, top 48% 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, 3 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

4 authors at 1 institution in 1 country.

Yuki TatenoDepartment of Surgery, Tama-nambu Regional Hospital, 2-1-2, Nakazawa, Tama, Tokyo, Japan. ytateno13044@gmail.com.
Kimito HaradaDepartment of Surgery, Tama-nambu Regional Hospital, 2-1-2, Nakazawa, Tama, Tokyo, Japan.
Fumiki OkamotoDepartment of Surgery, Tama-nambu Regional Hospital, 2-1-2, Nakazawa, Tama, Tokyo, Japan.
Hideo KatsuragawaDepartment of Surgery, Tama-nambu Regional Hospital, 2-1-2, Nakazawa, Tama, Tokyo, Japan.
Tama Art University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccording to previous reports, surgery is not recommended until at least 4 weeks after the symptoms of coronavirus disease 2019 resolve. However, strong evidence has not been established regarding the optimal timing and preoperative examination for elective laparoscopic colectomy for colorectal cancer in individuals with a previous coronavirus disease 2019 infection. CASE PRESENTATION: A 63-year-old Asian man underwent elective laparoscopic colectomy for sigmoid colon cancer 3 weeks after asymptomatic coronavirus disease 2019. The postoperative course was good, and none of the surgical staff was infected with coronavirus disease 2019.

conclusionIn this patient infected with coronavirus disease 2019 within 4 weeks of surgery, preoperative venous ultrasound of the lower extremities and a chest computed tomography scan were useful examinations for ensuring a safe surgical procedure for the patient and the staff. Surgery within 4 weeks may be possible with careful selection of cases based on thorough preoperative examination. This report may contribute to the development of a consensus on performing safe elective colectomy for colon cancer in persons previously infected with coronavirus disease 2019.

Indexed as

COVID-19LaparoscopyColectomyHumansMaleMiddle AgedSARS-CoV-2Treatment OutcomeColorectal cancerCOVID-19Laparoscopic colectomy

Identifiers

PMID34006322
PMCPMC8130803
OpenAlexW3160026818

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.