SynthesisInternational wound journal2023
Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis.
Synthesis in International wound journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review.PLoS medicine · 2025Pooled it
- Incidence of pressure injury in patients with moderate and severe traumatic brain injury: a systematic review.BMJ open · 2024Pooled it
- Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis.International wound journal · 2023Pooled it
- Comparison of the Validity and Reliability of Five Pressure Injury Risk Scales in Intensive Care.Nursing in critical care · 2026Article
- Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.Journal of clinical nursing · 2026Article
- Early Detection of Deep Tissue Pressure Injury in Intensive Care Using Hemodynamics-Based Machine Learning: A Retrospective Cohort Study.International wound journal · 2026Observational
- Exploratory Analysis of Factors Associated With Pressure Injuries in Surgical Patients Assessed With the ELPO Scale.International journal of nursing practice · 2026Article
- Pressure Injury Risk Assessment in Nursing Practice: A Head-to-Head Comparison of the Braden Scale and Machine Learning Models.Journal of clinical medicine · 2026Article
- Early prediction of pressure injury risk in hospitalized patients using supervised machine learning models based on nursing records.Scientific reports · 2026Article
- Characteristics, risk factors, prevention and nursing measures of medical device-related pressure injuries in critically ill patients: a scoping review.Frontiers in public health · 2026Article
- Development of an explainable machine learning model for predicting device-related pressure injuries in clinical settings.BMC medical informatics and decision making · 2025Article
- Observational
- Risk prediction tools for pressure injury occurrence: an umbrella review of systematic reviews reporting model development and validation methods.Diagnostic and prognostic research · 2025Review
- Development and validation of a nomogram for pressure injury risk prediction in stroke patients: a retrospective cohort study.Frontiers in neurology · 2025Article
- An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations.Computers in biology and medicine · 2024Article
- Enhancing clinical reliability in pressure injury prediction: A conformal prediction approach with machine learning models.Digital healthArticle
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
Despite the fact that machine learning (ML) algorithms to construct predictive models for pressure injury development are widely reported, the performance of the model remains unknown. The goal of the review was to systematically appraise the performance of ML models in predicting pressure injury. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, Grey literature and other databases were systematically searched. Original journal papers were included which met the inclusion criteria. The methodological quality was assessed independently by two reviewers using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed with Metadisc software, with the area under the receiver operating characteristic curve, sensitivity and specificity as effect measures. Chi-squared and I
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.