Evidence map›Paper›PMID 38526647›Full record

ArticlePhysical and engineering sciences in medicine2024

Machine learning-based analysis of

Maziar Khateri, Farshid Babapour Mofrad, Parham Geramifar, Elnaz Jenabi

Abstract read
PubMed Publisher
In one paragraph

Article in Physical and engineering sciences in medicine, 2024. 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
2.4field-weighted citation impact, top 12% 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, 6 citations in OpenAlex.

  1. Review
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

4 authors at 2 institutions in 1 country.

Maziar KhateriDepartment of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Farshid Babapour MofradDepartment of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. Babapour@srbiau.ac.ir.ORCID http://orcid.org/0000-0002-5892-7971
Parham GeramifarResearch Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Elnaz JenabiResearch Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Islamic Azad University, Science and Research Branch · IRTehran University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on

Indexed as

Gallium IsotopesGallium RadioisotopesMachine LearningNeoplasm GradingPositron Emission Tomography Computed TomographyProstatic NeoplasmsAgedEdetic AcidHumansImage Processing, Computer-AssistedMaleMiddle AgedOligopeptidesROC CurveEdetic Acidgallium 68 PSMA-11Gallium IsotopesGallium RadioisotopesOligopeptidesFeature SelectionGa-PSMAPET/CTProstate Cancer (PCa)Radiomics

Identifiers

PMID38526647
OpenAlexW4393162115

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.