Evidence map›Paper›PMID 20432040›Full record

ArticleEuropean radiology2010

Identifying the most infectious lesions in pulmonary tuberculosis by high-resolution multi-detector computed tomography.

Jun Jun Yeh, Solomon Chih-Cheng Chen, Wen-Bao Teng, Chun-Hsiung Chou, Shih-Peng Hsieh, Tsung-Lung Lee, Ming-Ting Wu

Abstract readEvaluation Study
PubMed Publisher
In one paragraph

Article in European radiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

15 citing papers in PubMed, 41 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

7 authors at 2 institutions in 1 country.

Jun Jun YehPingtung Christian Hospital, Pingtung, Taiwan.
Solomon Chih-Cheng Chen
Wen-Bao Teng
Chun-Hsiung Chou
Shih-Peng Hsieh
Tsung-Lung Lee
Ming-Ting Wu
Pingtung Christian Hospital · TWNational Yang Ming Chiao Tung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to determine whether characteristics detected by multi-detector computed tomography (MDCT) were predictive of highly infectious, smear-positive, active pulmonary tuberculosis (PTB).

methodsAmong 124 patients with active PTB, 84 had positive (group 1) and 40 had negative (group 2) smear results for acid-fast bacilli. Multiplanar MDCT, axial conventional CT and chest X-ray images were analysed retrospectively for morphology, number, and segmental (lobe) distribution of lesions.

resultsBy multivariate analysis, consolidation over any segment of the upper, middle, or lingual lobes, cavitations, and clusters of nodules were associated with group 1, while centrilobular nodules were predictive of group 2. Using five independent variables associated with risk in group 1, a prediction model was created to distinguish between group 1 and group 2. ROC curve analysis showed an area under the curve of 0.951 +/- 0.021 for this prediction model. With the ideal cutoff point score of 1, the sensitivity, specificity, and positive predictive values were 84.5%, 97.5%, and 98.0%, respectively.

conclusionsA model to predict smear-positive active PTB on the basis of findings from MDCT may be a useful tool for clinical decisions about isolating patients pending sputum smear results.

Indexed as

Bacterial LoadDiagnosis, DifferentialFemaleHumansLungMaleMiddle AgedPattern Recognition, AutomatedRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificityTomography, X-Ray ComputedTuberculosis, Pulmonary

Identifiers

PMID20432040
OpenAlexW2063468093

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.