Evidence map›Paper›PMID 25364649›Full record

ArticleIntractable & rare diseases research2014

Noninvasive prenatal testing in China: Future detection of rare genetic diseases?

Lin Mei, Qi Tang, Baiyu Sun, Lingzhong Xu

Open access · bronzeAbstract read
In one paragraph

Article in Intractable & rare diseases research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 13% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

4 authors at 2 institutions in 1 country.

Lin MeiDepartment of Health Care Management and Maternal and Child Health, Shandong University, Ji'nan, Shandong, China; ; School of Stomatology, Shandong University, Ji'nan, Shandong, China.
Qi TangDepartment of Health Care Management and Maternal and Child Health, Shandong University, Ji'nan, Shandong, China;
Baiyu SunSchool of Stomatology, Shandong University, Ji'nan, Shandong, China.
Lingzhong XuDepartment of Health Care Management and Maternal and Child Health, Shandong University, Ji'nan, Shandong, China;
Shandong Maternal and Child Health Hospital · CNShandong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Noninvasive prenatal testing (NIPT) provides an innovative method to detect genetic conditions in fetuses using a maternal blood sample, thus avoiding the risk of miscarriage associated with traditional invasive procedures. Since 80% of rare diseases are genetic diseases, NIPT has the potential to detect rare genetic diseases early on and it has been used in many countries and regions. Since China has the world's largest population of patients with rare diseases, NIPT has been implemented in China since 2010. However, the regulations governing NIPT in China are weak and NIPT oversight and research are still lacking. Strict registration is needed to ensure the quality of NIPT, additional certification can help a developer/manufacturer of an NIPT test to compile clinical data and to improve innovation, and academic societies can provide committee opinions that are suited to the current situation in China. These efforts may improve regulations governing NIPT and NIPT oversight and research in China. With these improvements, NIPT may offer promise in terms of the early detection of rare diseases.

Indexed as

Clinical Laboratory Improvement Amendments (CLIA)Committee opinionLaboratory Developed Tests (LDTs)Premarket notification

Identifiers

PMID25364649
PMCPMC4214242
OpenAlexW2126143277

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.