Evidence map›Paper›PMID 32029876›Full record

ArticleScientific reports2020

A valuable computed tomography-based new diagnostic tool for severe chest lesions in active pulmonary tuberculosis: combined application of influencing factors.

Kui Li, Zicheng Jiang, Yanan Zhu, Chuanqi Fan, Tao Li, Wenqi Ma, Yingli He

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.2field-weighted citation impact, top 35% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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 3 institutions in 1 country.

Kui LiDepartment of Infectious Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China.ORCID http://orcid.org/0000-0002-5684-3918
Zicheng JiangDepartment of Infectious Diseases, Ankang Central Hospital, 85 South Jinzhou Road, Ankang, 725000, Shaanxi, China.
Yanan ZhuThe Medical Imaging Centre, Ankang Central Hospital, 85 South Jinzhou Road, Ankang, 725000, Shaanxi, China.
Chuanqi FanDepartment of Infectious Diseases, Ankang Central Hospital, 85 South Jinzhou Road, Ankang, 725000, Shaanxi, China.
Tao LiDepartment of Infectious Diseases, Ankang Central Hospital, 85 South Jinzhou Road, Ankang, 725000, Shaanxi, China.
Wenqi MaDepartment of Ultrasound, The Second Affiliated Hospital of Xi'an Jiaotong University, 157 West 5 Road, Xi'an, 710004, Shaanxi, China.
Yingli HeDepartment of Infectious Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China. heyingli2001@163.com.
Ankang City Central Hospital · CNFirst Affiliated Hospital of Xi'an Jiaotong University · CNSecond Affiliated Hospital of Xi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The objective of the present investigation was to explore the influencing factors and value of computed tomography (CT) for diagnosing severe chest lesions in active pulmonary tuberculosis (APTB). This retrospective investigation included 463 patients diagnosed with APTB. Relevant clinical features were collected. Patients were assigned to mild/moderate group or advanced group depending on the lesion severity on chest CT, severe chest CT lesion refers to the moderately dense or less diffuse lesion that exceeds the total volume of one lung, or the dense fusion lesion greater than one third of the volume of one lung, or the lesion with cavity diameter ≥4 cm. Independent risk factors for severe lesions were determined by univariate and multivariate logistic regression analyses, and the diagnostic efficiency of the risk factors was assessed by receiver operating characteristic curve (ROC). Chest CT demonstrated that there were 285 (61.56%) cases with severe lesions; multivariate Logistic regression analysis showed dust exposure [odds ratio (OR) = 4.108, 95% confidence interval (CI) 2.416-6.986], patient classification (OR = 1.792, 95% CI 1.067-3.012), age (OR = 1.018, 95% CI 1.005-1.030), and albumin-globulin ratio (OR = 0.179, 95% CI 0.084-0.383) to be independently correlated with severe lesions on chest CT. ROC curve analysis showed the cutoff values of age, albumin-globulin ratio and combined score to be 39 years, 0.918 and -0.085, respectively. The predictive value of combined score area under the curve 0.753 (95% CI 0.708-0.798) was higher than that of any single factor. The combined score of these four factors further improved the predictive efficacy.

Indexed as

Tomography, X-Ray ComputedAdultAge FactorsBiomarkersDustEnvironmental ExposureFemaleHumansLungMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk FactorsROC CurveSerum Albumin, HumanBiomarkersDustSerum Albumin, HumanSerum Globulins

Identifiers

PMID32029876
PMCPMC7005193
OpenAlexW3037484830

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.