Evidence map›Paper›PMID 37759332›Full record

ArticleBMC cancer2023

Early prediction of response to palliative chemotherapy in patients with stage-IV gastric and esophageal cancer.

Xiaoyuan Ma, Eric Pierce, Harsh Anand, Natalie Aviles, Paul Kunk, Negin Alemazkoor

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2023. 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
1.6field-weighted citation impact, top 16% 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, 6 citations in OpenAlex.

  1. Review
  2. 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

6 authors at 2 institutions in 1 country.

Xiaoyuan MaDepartment of Statistics, University of Virginia, Charlottesville, USA.
Eric PierceSchool of Medicine, University of Virginia, Charlottesville, USA.
Harsh AnandSystem and Information Engineering, University of Virginia, Charlottesville, USA.
Natalie AvilesDepartment of Sociology, University of Virginia, Charlottesville, USA.
Paul KunkSchool of Medicine, University of Virginia, Charlottesville, USA.
Negin AlemazkoorSystem and Information Engineering, University of Virginia, Charlottesville, USA. na7fp@virginia.edu.
University of Virginia · USUniversity of Virginia Health System · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe goal of therapy for many patients with advanced stage malignancies, including those with metastatic gastric and esophageal cancers, is to extend overall survival while also maintaining quality of life. After weighing the risks and benefits of treatment with palliative chemotherapy (PC) with non-curative intent, many patients decide to pursue treatment. It is known that a subset of patients who are treated with PC experience significant side effects without clinically significant survival benefits from PC.

methodsWe use data from 150 patients with stage-IV gastric and esophageal cancers to train machine learning models that predict whether a patient with stage-IV gastric or esophageal cancers would benefit from PC, in terms of increased survival duration, at very early stages of the treatment.

resultsOur findings show that machine learning can predict with high accuracy whether a patient will benefit from PC at the time of diagnosis. More accurate predictions can be obtained after only two cycles of PC (i.e., about 4 weeks after diagnosis). The results from this study are promising with regard to potential improvements in quality of life for patients near the end of life and a potential overall survival benefit by optimizing systemic therapy earlier in the treatment course of patients.

Indexed as

Drug-Related Side Effects and Adverse ReactionsEsophageal NeoplasmsStomach NeoplasmsHumansQuality of LifeEsophageal and gastric cancerMachine learningMetastatic cancerPalliative chemotherapyResponse prediction

Identifiers

PMID37759332
PMCPMC10536729
OpenAlexW4387079826

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.