In one paragraphArticle in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
18 authors.
Elisa Nuez-ZaragozaClinical Laboratory Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0000-0002-0254-2175 Indira Bhambi-BlancoClinical Laboratory Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0000-0002-8593-3381 Mònica Vidal-PlaClinical Laboratory Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0000-0002-2411-7559 Isabel Aparicio-CalventeClinical Laboratory Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0009-0006-1734-1954 M Rosa Escoda-GiraltPathology Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.
Joana Gallardo-CamposPathology Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.
Joan Carles FerreresPathology Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0000-0001-7742-0239 Luis FrisanchoDigestive System Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0009-0002-0346-5068 Laia Mas-MaresmaInternal Medicine Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0000-0003-4963-7072 Patricia Aguilera-FernándezInternal Medicine Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.
Sonia Marco-ContinenteIntensive Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0009-0005-7540-5285 Marina Sierra-BoadaOncology Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0009-0006-6953-8505 Pablo Andreu-CoboOncology Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0009-0003-1613-9306 Miquel GallegoRespiratory Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Barcelona, Spain.ORCID https://orcid.org/0000-0002-8882-4033 Francesca CanaliasDepartament de Bioquímica i Biologia Molecular, Laboratori de Referència d'Enzimologia Clínica, Universitat Autònoma de Barcelona, Bellaterra, Spain.
Jaume TrapéLaboratory Medicine Department, Faculty of Medicine, ALTHAIA Xarxa Assitencial Universitària de Manresa, Universitat de Vic-Universitat Central de Catalunya, Manresa, Spain.ORCID https://orcid.org/0000-0003-4018-6906 Funding
Fundación Jose Luis Castaño (SEQC)Parc Taulí Research and Innovation Institute (I3PT)
6 · The paper itselfAbstract
New diagnostic tools, such as high fluorescence cells (HFc) and tumor markers (TMs), have emerged in the quest to accurately diagnose malignant pleural effusion (MPE). This study evaluated the diagnostic performance of the HFc and TMs combination. Hospitalized patients with pleural effusion were recruited at Parc Taulí University Hospital in Sabadell (Spain). Pleural fluid (PF) and serum samples were collected and analyzed in the Clinical Laboratory, and PF samples were sent to the Pathology Department for cytological examination. PF cell counts were determined using an XN-10 analyzer (Sysmex, Kobe, Japan); TMs (CEA, CA 19.9, CA 15.3, CA 72.4, and CYFRA 21.1) were assessed using Cobas e801 (Roche Diagnostics, Mannheim, Germany). An MPE diagnosis was made based on positive cytology and/or positive pleural biopsy. A diagnostic algorithm was developed to detect MPE starting with an initial HFc screening, followed by TM determination with specific cutoffs depending on fluid type. The study included 300 pleural effusions from 209 patients. The algorithm had a sensitivity of 68% and a negative predictive value of 93%; its specificity and positive predictive value were both 100%. When combined with cytology, the sensitivity of the algorithm increased to 85%, and its negative predictive value to 96%. Our algorithm exhibited promising diagnostic performance and could serve as a non-invasive tool for MPE detection. HFc screening helps identify potential MPE cases, and TM assessment, with its high specificity, contributes to MPE confirmation. Furthermore, establishing different TM cutoffs based on fluid type increases sensitivity without reducing specificity by introducing false positives.
Indexed as
Biomarkers, TumorPleural Effusion, MalignantAdultAgedAged, 80 and overAlgorithmsFemaleHumansMaleMiddle AgedSensitivity and SpecificityBiomarkers, Tumorhigh fluorescence cellsmalignant pleural effusiontumor markers
Identifiers
PMID42304189
PMCPMC13272102
What OpenQuestion holds
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LicenceCC BY-NC
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