Evidence map›Paper›PMID 37872641›Full record

ArticleEuropean journal of medical research2023

Predictors of cervical tumour size for outpatients with cervical cancer at the University of Gondar referral hospital: a retrospective study design.

Chalachew Gashu, Aragaw Eshetie Aguade

Open access · goldAbstract read
In one paragraph

Article in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 40% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

2 authors at 1 institution in 1 country.

Chalachew GashuDepartment of Statistics, College of Natural & Computational Sciences, Oda Bultum University, Chiro, Ethiopia. chalachew154@gmail.com.
Aragaw Eshetie AguadeDepartment of Statistics, College of Natural & Computational Sciences, University of Gondar, Gondar, Ethiopia.
University of Gondar · ET

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCervical cancer is one of the most serious threats to women's lives. Modelling the change in tumour size over time for outpatients with cervical cancer was the study's main goal.

methodsA hospital conducted a retrospective cohort study with outpatients who had cervical cancer. The information about the tumour size was taken from the patient's chart and all patient data records between May 20, 2017, and May 20, 2021. The data cover 322 cervical cancer outpatients' basic demographic and medical information. When analysing longitudinal data, the linear mixed effect model and the connection between tumour sizes in outpatients were taken into consideration. A linear mixed model, a random intercept model, and a slope model were used to fit the data.

resultA sample of 322 cervical cancer outpatients was examined, and 148 (or 46% of the outpatients) tested positive for HIV. The linear mixed model with a first-order autoregressive covariance structure revealed that a change in time of one month led to a 0.009 cm

conclusionAs a consequence, there was a significant association between the longitudinal change in tumour size and the predictor variables visit time, therapy, patient weight, cancer stage, HIV, oral contraceptive use, history of abortion, and smoking status.

Indexed as

HIV InfectionsUterine Cervical NeoplasmsFemaleHospitalsHumansOutpatientsPregnancyReferral and ConsultationRetrospective StudiesCervical cancerLinear mixed modelLongitudinal data analysisTumour size

Identifiers

PMID37872641
PMCPMC10594753
OpenAlexW4387867427

What OpenQuestion holds

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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.