ArticleTherapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy2025
Text mining for case report articles on "peritoneal dialysis" from PubMed database.
Article in Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Exploring Research Trends in Chronic Kidney Disease and Sedentary Behavior: A Text Mining Analysis of Article Titles.Cureus · 2026Review
- Text mining for case report articles on "peritoneal dialysis" from PubMed database.Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy · 2025Article
- Optimizing CAPD Patient Monitoring Through Automated Vs Rule-Based Artificial Intelligence: A Systematic Comparative Review.International journal of nephrology and renovascular disease · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
introductionThe number of published medical articles on peritoneal dialysis (PD) has been increasing, and efficiently selecting information from numerous articles can be difficult. In this study, we examined whether artificial intelligence (AI) text mining can be a good support for efficiently collecting PD information.
methodsWe performed text mining and analyzed all the abstracts of case reports on PD in the PubMed database. In total, 3137 case reports with abstracts related to "peritoneal dialysis" published from 1970 to 2021 were identified.
resultsA total of 280 347 relevant words were extracted from all the abstracts. Word frequency analysis, word dependency analysis, and word frequency transition analysis showed that peritonitis, encapsulating peritoneal sclerosis, and child have been important keywords. Theseanalyses not only reflected historical background but also anticipated future trends of PD study.
conclusionThese suggest that text mining can be a good support for efficiently collecting PD information.
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.