ArticleBMC pulmonary medicine2022
A scoring model for diagnosis of tuberculous pleural effusion.
Article in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Development of a diagnostic nomogram for tuberculous pleural effusion.Journal of clinical tuberculosis and other mycobacterial diseases · 2026Article
- Development and validation of a simplified machine learning model based on T-SPOT.TB and routine clinical data for the diagnosis of tuberculous pleural effusion.Journal of thoracic disease · 2026Article
- Transthoracic ultrasound features to differentiate tuberculous from malignant pleural effusion: development of a machine learning-based clinical prediction model.Journal of thoracic disease · 2026Article
- Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion.Frontiers in medicine · 2025Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDue to the low efficiency of a single clinical feature or laboratory variable in the diagnosis of tuberculous pleural effusion (TBPE), the diagnosis of TBPE is still challenging. This study aimed to build a scoring diagnostic model based on laboratory variables and clinical features to differentiate TBPE from non-tuberculous pleural effusion (non-TBPE).
methodsA retrospective study of 125 patients (63 with TBPE; 62 with non-TBPE) was undertaken. Univariate analysis was used to select the laboratory and clinical variables relevant to the model composition. The statistically different variables were selected to undergo binary logistic regression. Variables B coefficients were used to define a numerical score to calculate a scoring model. A receiver operating characteristic (ROC) curve was used to calculate the best cut-off value and evaluate the performance of the model. Finally, we add a validation cohort to verify the model.
resultsSix variables were selected in the scoring model: Age ≤ 46 years old (4.96 points), Male (2.44 points), No cancer (3.19 points), Positive T-cell Spot (T-SPOT) results (4.69 points), Adenosine Deaminase (ADA) ≥ 24.5U/L (2.48 point), C-reactive Protein (CRP) ≥ 52.8 mg/L (1.84 points). With a cut-off value of a total score of 11.038 points, the scoring model's sensitivity, specificity, and accuracy were 93.7%, 96.8%, and 99.2%, respectively. And the validation cohort confirms the model with the sensitivity, specificity, and accuracy of 92.9%, 93.3%, and 93.1%, respectively.
conclusionThe scoring model can be used in differentiating TBPE from non-TBPE.
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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.