Evidence map›Paper›PMID 31916312›Full record

ArticleJournal of clinical laboratory analysis2020

Novel classifiers with clinical laboratory parameters for early detection of osteosarcoma.

Lin-Lin Cao, Zhaoming Chen, Zhihong Yue, Lin Pei, Mei Jia, Hui Wang, Tingting Li

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

Article in Journal of clinical laboratory analysis, 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
0.6field-weighted citation impact, top 28% 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, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

7 authors at 1 institution in 1 country.

Lin-Lin CaoDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.ORCID https://orcid.org/0000-0003-2958-6263
Zhaoming ChenDepartment of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.
Zhihong YueDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Lin PeiDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Mei JiaDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Hui WangDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Tingting LiDepartment of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.
Peking University · CN

Funding

National Key Research and Development Program of China 2018YFA0507504National Natural Science Foundation of China 61773025National Natural Science Foundation of China 81702788
6 · The paper itself

Abstract

backgroundOsteosarcoma (OS) is one of the most common malignant bone tumors. It is essential to explore early diagnostic indicators with high sensitivity and specificity due to the rapid progression and metastasis of OS and the poor survival of metastatic OS patients. However, a few indicators of diagnostic significance have been described.

methodsA total of 458 OS patients, 312 healthy individuals, and 228 patients with primary benign bone lesions were included. Logistic regression was performed on 46 clinical laboratory parameters to establish the diagnostic classifiers, which were evaluated by analysis of the receiver operating characteristic (ROC) curves.

resultsWe established three diagnostic classifiers, called C

conclusionOur study suggests the potential of C

Indexed as

AdolescentAdultAlkaline PhosphataseBiomarkers, TumorBone NeoplasmsCase-Control StudiesChildEarly Detection of CancerFemaleHumansMaleOsteosarcomaReproducibility of ResultsROC CurveSensitivity and SpecificityYoung AdultAlkaline PhosphataseBiomarkers, Tumoralkaline phosphataseclassifierdiagnosislaboratory parameterosteosarcoma

Identifiers

PMID31916312
PMCPMC7246378
OpenAlexW3000599872

What OpenQuestion holds

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LicenceCC BY-NC
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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.