ArticleTherapeutic advances in respiratory disease
A simple and efficient clinical prediction scoring system to identify malignant pleural effusion.
Article in Therapeutic advances in respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 2 citations in OpenAlex.
- Incremental predictive value of a CT-based deep learning radiomics model for differentiating benign and malignant pleural effusions.Frontiers in medicine · 2026Article
- Development of a scoring system based on a nomogram to identify malignant pleural effusion.Journal of thoracic disease · 2025Article
- Predictive model for sarcopenia in patients with non-small cell lung cancer and malignant pleural effusion.BMC cancer · 2025Article
- Development and validation of a prediction model based on a nomogram for tuberculous pleural effusion.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly diagnosis of malignant pleural effusion (MPE) is of great significance. Current prediction models are not simple enough to be widely used in heavy clinical work.
objectivesWe aimed to develop a simple and efficient clinical prediction scoring system to distinguish MPE from benign pleural effusion (BPE).
designThis retrospective study involved patients with MPE or BPE who were admitted in West China Hospital from December 2010 to September 2016.
methodsPatients were divided into training, testing, and validation set. Prediction model was developed from training set and modified to a scoring system. The diagnostic efficacy and clinical benefits of the scoring system were estimated in all three sets.
resultsFinally, 598 cases of MPE and 1094 cases of BPE were included. Serum neuron-specific enolase, serum cytokeratin 19 fragment (CYFRA21-1), pleural carcinoembryonic antigen (CEA), and ratio of pleural CEA to serum CEA were selected to establish the prediction models in training set, which were modified to the scoring system with scores of 6, 8, 10, and 9 points, respectively. Patients with scores >12 points have high MPE risk while ⩽12 points have low MPE risk. The scoring system has a high predictive value and good clinical benefits to differentiate MPE from BPE or lung-specific MPE from BPE.
conclusionThis study developed a simple clinical prediction scoring system and was proven to have good clinical benefits, and it may help clinicians to separate MPE from BPE.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.