Evidence map›Paper›PMID 26622372›Full record

ArticleExperimental and therapeutic medicine2015

Neutrophil elastase and fetal fibronectin levels as predictors of single-birth prematurity.

Fang Ai, Gui-Qing Li, Jiang Jiang, Xu-Dong Dong

Open access · diamondAbstract read
In one paragraph

Article in Experimental and therapeutic medicine, 2015. 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
0.2field-weighted citation impact, top 41% 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, 4 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Fang AiDepartment of Obstetrics, The First People's Hospital of Yunnan, Kunming, Yunnan 650032, P.R. China.
Gui-Qing LiDepartment of Obstetrics, The First People's Hospital of Yunnan, Kunming, Yunnan 650032, P.R. China.
Jiang JiangDepartment of Obstetrics, The First People's Hospital of Yunnan, Kunming, Yunnan 650032, P.R. China.
Xu-Dong DongDepartment of Obstetrics, The First People's Hospital of Yunnan, Kunming, Yunnan 650032, P.R. China.
First People's Hospital of Yunnan Province · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to investigate the predictive values (PVs) of neutrophil elastase (NE) and fetal fibronectin (fFN) in cervical secretions for single-birth premature delivery. Samples of cervical secretions were obtained from 144 women with high-risk singleton pregnancies at 20-34 weeks' gestation and premature Creasy scores of >12 points for NE and fFN level testing, and the PVs of the two indicators for premature birth (PB) were retrospectively analyzed. NE and fFN had high negative PVs (NPVs) for PB; the NPV of NE and fFN for delivery 7 days after detection was significantly higher than the positive PV (P<0.01). In addition, the sensitivity of the combined use of NE and fFN levels for PB prediction was high if both were present, and the PB rate of the double-positive group was higher than that of the single-positive group (P<0.01). Clinical intervention could turn the NE and fFN values negative in certain cases; in these cases, the PB rate was significantly lower than that in the sustained-positive group. In conclusion, NE and fFN in cervical secretions could be used as objective predictors of premature delivery, and their combined application could improve the prediction sensitivity. Effective clinical intervention could then reduce the incidence of PB.

Indexed as

clinical interventionfetal fibronectinneutrophil elastasepredictionpremature birth

Identifiers

PMID26622372
PMCPMC4509021
OpenAlexW1587281335

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.