ArticleFrontiers in physiology2021
Artificial Intelligence May Predict Early Sepsis After Liver Transplantation.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- AI-Based Sepsis Prediction in Hospitalized Adults: Systematic Review, Subgroup Meta-Analysis, and Contextual Analysis of Clinical Burden.Journal of medical Internet research · 2026Pooled it
- Investigating the role of artificial intelligence in predicting risk factors after liver transplantation: A systematic review.Canadian liver journal · 2026Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Intuitive Risk Equation for Post-Transplant Bloodstream Infection Prediction: A Symbolic Regression Approach.Biomedicines · 2026Article
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Evolving Transplant Oncology: Evolving Criteria for Better Decision-Making.Diagnostics (Basel, Switzerland) · 2025Review
- AI reshaping life sciences: intelligent transformation, application challenges, and future convergence in neuroscience, biology, and medicine.Frontiers in digital health · 2025Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.Biomedical signal processing and control · 2024Article
- A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.Critical care (London, England) · 2024Article
- Development and validation of a practical machine learning model to predict sepsis after liver transplantation.Annals of medicine · 2023Article
- A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients.Critical care explorations · 2023Article
- Evaluating machine learning models for sepsis prediction: A systematic review of methodologies.iScience · 2022Article
- Machine learning models for predicting in-hospital mortality in patient with sepsis: Analysis of vital sign dynamics.Frontiers in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.