ArticleIntensive care medicine2019
The use of clustering algorithms in critical care research to unravel patient heterogeneity.
Article in Intensive care medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Self-Reported Quality of Life and 30-Day Mortality in Elective Cardiac Surgery.JACC. Advances · 2026Article
- Challenges in early detection and prognostication of sepsis: new approaches from the emergency department and intensive care unit.EClinicalMedicine · 2026Review
- Identifying distinct clusters of ICU survivors by integrating demographic, pre-admission quality of life, and clinical data: a large prospective cohort study.Intensive care medicine · 2025Article
- Subphenotyping prone position responders with machine learning.Critical care (London, England) · 2025Observational
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- Differential effects of thiamine and ascorbic acid in clusters of septic patients identified by latent variable analysis.Critical care (London, England) · 2024Article
- Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database.Heliyon · 2024Article
- Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies.Critical care medicine · 2024Review
- Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.Scientific reports · 2024Article
- Review
- Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts.Scientific reports · 2024Article
- Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records.JMIR formative research · 2023Article
- Associated Risk Factors and Impact in Clinical Outcomes of Multiorgan Failure in Patients with TBI.Neurocritical care · 2023Observational
- Clinical Sepsis Phenotypes in Critically Ill Patients.Microorganisms · 2023Review
- Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU.Intensive care medicine · 2022Article
- Observational
- Electronic nudge tool technology used in the critical care and peri-anaesthetic setting: a scoping review protocol.BMJ open · 2022Article
- Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.European journal of pediatrics · 2022Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
31062051What 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.