ReviewHealth care science2026
Machine Learning Methods to Predict the Length of Stay for Acute Stroke: A Scoping Review and Meta-Analysis.
Review in Health care science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Machine Learning Methods to Predict the Length of Stay for Acute Stroke: A Scoping Review and Meta-Analysis.Health care science · 2026Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reliable hospital length of stay (LOS) predictions improve patient outcomes and optimize clinical pathways in stroke care. There is limited evidence systematically investigating benefits and limitations of existing machine learning (ML) models for determining factors associated with LOS. We conducted a scoping review to: (1) investigate factors affecting LOS in stroke to identify key predictors for enhancing ML models; and (2) assess the performance of predictive models (ML and traditional models) to generate insights for improving LOS predictions. This review was undertaken in Ovid MEDLINE, Ovid Embase, and Scopus, searching English-language records from 2014 to 2024 using the search terms "stroke" and "length of stay". We included studies investigating associated factors or assessing modeling approaches for predicting LOS in stroke. A meta-analysis using Bayesian methods was conducted to compare predictive performance (e.g., pooled
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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.