Evidence map›Paper›PMID 27752286›Full record

ArticleBioData mining2016

Low-mass-ion discriminant equation (LOME) for ovarian cancer screening.

Jun Hwa Lee, Byong Chul Yoo, Yun Hwan Kim, Sun-A Ahn, Seung-Gu Yeo, Jae Youl Cho, Kyung-Hee Kim, Seung Cheol Kim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Jun Hwa Lee *Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ewha Womans University Mokdong Hospital, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
Byong Chul Yoo *Colorectal Cancer Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea.
Yun Hwan Kim *Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ewha Womans University Mokdong Hospital, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
Sun-A AhnColorectal Cancer Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea.
Seung-Gu YeoDepartment of Radiation Oncology, Soonchunhyang University College of Medicine, Cheonan, Republic of Korea.
Jae Youl ChoDepartment of Genetic Engineering, Sungkyunkwan University, Suwon, Gyeonggi Republic of Korea.
Kyung-Hee KimColorectal Cancer Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi Republic of Korea.
Seung Cheol KimDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ewha Womans University Mokdong Hospital, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA low-mass-ion discriminant equation (LOME) was constructed to investigate whether systematic low-mass-ion (LMI) profiling could be applied to ovarian cancer (OVC) screening.

resultsMatrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry was performed to obtain mass spectral data on metabolites detected as LMIs up to a mass-to-charge ratio (

conclusionThe present study suggested that OVC LOME might be a useful non-invasive tool with high sensitivity and specificity for OVC screening. The LOME approach could enable screening for multiple diseases, including various types of cancer, based on a single blood sample. Furthermore, the serum levels of three metabolic compounds-

Indexed as

MALDI-TOF mass spectrometryOvarian cancerPattern recognitionScreeningSerum profiling

Identifiers

PMID27752286
PMCPMC5059959

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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.