Evidence map›Paper›PMID 33379383›Full record

ReviewJournal of personalized medicine2020

Is It Possible to Personalize the Diagnosis and Treatment of Breast Cancer during Pregnancy?

Petra Tesarova, David Pavlista, Antonin Parizek

Open access · goldAbstract readReview
In one paragraph

Review in Journal of personalized medicine, 2020. 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 35% 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, 8 citations in OpenAlex.

  1. Review
  2. Review
  3. Innovations in the Integrated Management of Breast Cancer.Journal of personalized medicine · 2022
    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

3 authors at 1 institution in 1 country.

Petra TesarovaDepartment of Oncology, 1st Faculty of Medicine Charles University and General University Hospital, 128 08 Prague, Czech Republic.
David PavlistaDepartment of Gynecology and Obstetrics, 1st Faculty of Medicine Charles University and General University Hospital, 128 08 Prague, Czech Republic.
Antonin ParizekDepartment of Gynecology and Obstetrics, 1st Faculty of Medicine Charles University and General University Hospital, 128 08 Prague, Czech Republic.
Charles University · CZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main goal of precision medicine in patients with breast cancer is to tailor the treatment according to the particular genetic makeup and the genetic changes in the cancer cells. Breast cancer occurring during pregnancy (BCP) is a complex and difficult clinical problem. Although it is not very common, both maternal and fetal outcome must be always considered when planning treatment. Pregnancy represents a significant barrier to the implementation of personalized treatment for breast cancer. Tailoring therapy mainly takes into account the stage of pregnancy, the subtype of cancer, the stage of cancer, and the patient's preference. Results of the treatment of breast cancer in pregnancy are as yet not very satisfactory because of often delayed diagnosis, and it usually has an unfavorable outcome. Treatment of patients with pregnancy-associated breast cancer should be centralized. Centralization may result in increased experience in diagnosis and treatment and accumulated data may help us to optimize the treatment approaches, modify general treatment recommendations, and improve the survival and quality of life of the patients.

Indexed as

breast cancerchemotherapypersonalizationpregnancytailoring

Identifiers

PMID33379383
PMCPMC7823967
OpenAlexW3116706039

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.