ArticleGels (Basel, Switzerland)2025
Interpretable Prediction and Analysis of PVA Hydrogel Mechanical Behavior Using Machine Learning.
Article in Gels (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Data-Driven Development of Biomedical Hydrogels for Controlled Drug Delivery: Clinical Applications and Emerging Machine-Learning Approaches.Journal of functional biomaterials · 2026Review
- Modeling of Biomechanical and Functional Parameters of Hydrogel-Cell Composites Fabricated by 3D Bioprinting Using AI-Supported Approach.Materials (Basel, Switzerland) · 2026Article
- Article
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- Multifunctional Nanomaterial-Integrated Hydrogels for Sustained Drug Delivery: From Synthesis and Characterization to Biomedical Application.Gels (Basel, Switzerland) · 2025Review
- A precision health approach to medication management in neurodevelopmental conditions: a model development and validation study using four international cohorts.medRxiv : the preprint server for health sciences · 2025Article
- AI-driven biomaterial design: an intelligent closed loop from reverse design to biological response.Frontiers in cell and developmental biology · 2025Review
- Machine learning model for unfavorable outcome prediction in neurosurgical patients: the potential role of liver function markers.Frontiers in neurologyArticle
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
Abstract
Polyvinyl alcohol (PVA) hydrogels have emerged as versatile materials due to their exceptional biocompatibility and tunable mechanical properties, showing great promise for flexible sensors, smart wound dressings, and tissue engineering applications. However, rational design remains challenging due to complex structure-property relationships involving multiple formulation parameters. This study presents an interpretable machine learning framework for predicting PVA hydrogel tensile strain properties with emphasis on mechanistic understanding, based on a comprehensive dataset of 350 data points collected from a systematic literature review. XGBoost demonstrated superior performance after Optuna-based optimization, achieving R
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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.