ArticleMalawi medical journal : the journal of Medical Association of Malawi2026
Bibliometric Analysis of Pulmonary Fibrosis Imaging Research: Knowledge Graph Construction Based on the Web of Science Core Database.
Article in Malawi medical journal : the journal of Medical Association of Malawi, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: To quantify publication trends, map international collaboration networks, and identify dominant and emerging research themes in Pulmonary fibrosis(PF) imaging (2015-2024). Methods: A structured Web of Science search combining PF- and imaging-related terms yielded 1,159 English-language original research articles. Analyses employed CiteSpace, VOSviewer, and Scimago Graphica for trend assessment, keyword co-occurrence, citation burst detection, and collaboration network visualization. Results: Annual publication volume showed sustained linear growth (R Conclusion: PF imaging research has shifted from diagnostic consensus toward quantitative CT biomarkers and artificial intelligence(AI)-driven phenotyping, driven by the need to reduce interobserver variability and enable individualized risk stratification. Geographic fragmentation and limited multicenter validation remain key barriers to AI generalizability. Future priorities include standardized imaging protocols, prospective multicenter validation cohorts, and integration of AI-driven CT phenotyping with multi-omics and circulating biomarkers for prognostic precision.
Indexed as
Identifiers
What Socratic 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 Socratic graph.