Evidence map›Paper›PMID 33502589›Full record

ReviewJournal of anesthesia2021

Preoperative assessment for scheduling surgery during the coronavirus disease pandemic.

Koichi Suehiro

Open access · bronzeAbstract readReview
In one paragraph

Review in Journal of anesthesia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 39% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Colorectal Surgery in the COVID-19 Pandemic Era.Journal of the anus, rectum and colon · 2022
    Review
  3. 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

1 author at 1 institution in 1 country.

Koichi SuehiroDepartment of Anesthesiology, Osaka City University Graduate School of Medicine, 1-5-7 Asahimachi, Abenoku, Osaka, Osaka, 545-8586, Japan. suehirokoichi@yahoo.co.jp.ORCID http://orcid.org/0000-0002-2156-6092
Osaka City University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

On Mar 11, 2020, the World Health Organization declared coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a pandemic. Because COVID-19 has a pre-symptomatic period of up to 2 weeks, SARS-CoV-2 infection has continued to spread. Some individuals with SARS-CoV-2 infection have a severe clinical course, while most individuals have mild or moderate symptoms. Because SARS-CoV-2 is transmitted via droplets and secretions, anesthesiologists have higher risks of infection, especially during airway management. Therefore, general anesthesia requiring airway management can be a challenging procedure for anesthesiologists. During the pandemic, many elective surgeries have been postponed or cancelled in most affected countries. Recently, the number of elective surgeries is gradually recovering from the effect of the COVID-19 pandemic, and hence, safe clinical practice and protocols to prevent SARS-CoV-2 transmission to medical staff should be established. This mini-review focuses on the preoperative assessment and decision with regard to scheduling surgery in elective and emergency cases during the COVID-19 pandemic. A standardized questionnaire and algorithm regarding COVID-19 should be used to assess surgical patients preoperatively as it increases the reproducibility and accuracy of the decision whether to proceed with surgery.

Indexed as

COVID-19PandemicsElective Surgical ProceduresHumansReproducibility of ResultsSARS-CoV-2AnesthesiaCOVID-19SARS-CoV-2Surgery

Identifiers

PMID33502589
PMCPMC7838845
OpenAlexW3122555520

What OpenQuestion holds

Textmetadata
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.