ArticleInternational journal of medical sciences2020
A Quantitative and Radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study.
Article in International journal of medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 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, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- Early prediction of moderate-to-severe BPD or death in preterm infants using minute-level respiratory monitoring and explainable machine learning.Pediatric research · 2026Article
- Identification and Functional Analysis of PANoptosis-Associated Genes in the Progression From Sepsis to ARDS.Immunity, inflammation and disease · 2025Article
- Enhancing physicians' radiology diagnostics of COVID-19's effects on lung health by leveraging artificial intelligence.Frontiers in bioengineering and biotechnology · 2023Article
- Developing medical imaging AI for emerging infectious diseases.Nature communications · 2022Article
- Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2022Article
- An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study.Frontiers in radiology · 2022Article
- Advances in medical imaging to evaluate acute respiratory distress syndrome.Chinese journal of academic radiology · 2022Review
- The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.Diagnostics (Basel, Switzerland) · 2021Review
- Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.European journal of radiology · 2021Article
- Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?Cureus · 2021Review
- CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia.BMC medical imaging · 2021Article
- Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis.Frontiers in medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 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.