Evidence map›Paper›PMID 36579541›Full record

ReviewJournal of personalized medicine2022

Personalized Medicine for Classical Anesthesia Drugs and Cancer Progression.

Bárbara Costa, Joana Mourão, Nuno Vale

Open access · goldAbstract readReview
In one paragraph

Review in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. 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

3 authors at 1 institution in 1 country.

Bárbara CostaOncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Doutor Plácido da Costa, 4200-450 Porto, Portugal.
Joana MourãoCentro de Investigação em Tecnologias e Serviços de Saúde (CINTESIS)@Health Research Network (RISE), Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal.
Nuno ValeOncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Doutor Plácido da Costa, 4200-450 Porto, Portugal.ORCID 0000-0002-1283-1042
Universidade do Porto · PT

Funding

CHAIR in Onco-Innovation at FMUPFCT and FEDER (European Union) award number IF/00092/2014/CP1255/CT0004FEDER through the COMPETE 2020-POCI, Portugal 2020 and Portuguese funds through FCT, in a framework of the of the projects in CINTESIS, R&D Unit and within the scope of the project "RISE-LA/P/0053/2020 reference UIDB/4255/2020
6 · The paper itself

Abstract

In this review, we aim to discuss the use and effect of five different drugs used in the induction of anesthesia in cancer patients. Propofol, fentanyl, rocuronium, sugammadex, and dexamethasone are commonly used to induce anesthesia and prevent pain during surgery. Currently, the mechanisms of these drugs to induce the state of anesthesia are not yet fully understood, despite their use being considered safe. An association between anesthetic agents and cancer progression has been determined; therefore, it is essential to recognize the effects of all agents during cancer treatment and to evaluate whether the treatment provided to the patients could be more precise. We also highlight the use of in silico tools to review drug interaction effects and safety, as well as the efficacy of the treatment used according to different subgroups of patients.

Indexed as

anesthesiacancer recurrencedrug interactionsin silico studiesmetastasispharmacodynamicpharmacokinetic

Identifiers

PMID36579541
PMCPMC9695346
OpenAlexW4308567761

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.