ArticleJournal of medical systems2017
Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression.
Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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
12 citing papers in PubMed, 3 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.BMC medical informatics and decision making · 2022Pooled it
- Towards the best kidney failure prediction tool: a systematic review and selection aid.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2020Pooled it
- Risk Prediction of Chronic Kidney Disease Progression in Type 2 Diabetes Mellitus Across Diverse Populations.NPJ digital medicine · 2026Article
- Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: a multi-center validation study.Scientific reports · 2025Observational
- Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.BMC nephrology · 2023Article
- Prediction of chronic kidney disease progression using recurrent neural network and electronic health records.Scientific reports · 2023Article
- Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease.Healthcare (Basel, Switzerland) · 2022Article
- Prediction models used in the progression of chronic kidney disease: A scoping review.PloS one · 2022Article
- Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease.Frontiers in medicine · 2022Article
- Supporting quality care for ESRD patients: the social worker can help address barriers to advance care planning.BMC nephrology · 2020Article
- Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods.Diagnostic and prognostic research · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic kidney disease (CKD) has attracted considerable attention in the public health domain in recent years. Researchers have exerted considerable effort in attempting to identify critical factors that may affect the deterioration of CKD. In clinical practice, the physical conditions of CKD patients are regularly recorded. The data of CKD patients are recorded as a high-dimensional time-series. Therefore, how to analyze these time-series data for identifying the factors affecting CKD deterioration becomes an interesting topic. This study aims at developing prediction models for stage 4 CKD patients to determine whether their eGFR level decreased to less than 15 ml/min/1.73m
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.