ArticleFrontiers in neurology2020
Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique.
Article in Frontiers in neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 18 citations in OpenAlex.
- Predictive models for trans-stenotic pressure gradient in the transverse sinus based on multimodal correlates.Quantitative imaging in medicine and surgery · 2026Article
- Comparison of the predictive performance of machine learning and conventional logistic regression models for poor discharge outcomes in patients with Aneurysmal subarachnoid hemorrhage: A retrospective cohort study.Neurosurgical review · 2026Article
- Machine learning modeling for outcome prediction of hospitalized patients with aneurysmal subarachnoid hemorrhage.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes.Neurosurgical review · 2025Article
- Applications of deep learning in intracranial aneurysm imaging: A scoping review of detection, risk prediction, and emerging prognostic models.Current journal of neurology · 2025Review
- Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms.Diagnostics (Basel, Switzerland) · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms.Scientific reports · 2023Article
- Natural course and risk factors of moyamoya disease with unruptured intracranial aneurysm.Frontiers in neurology · 2023Article
- A novel visual dynamic nomogram to online predict the risk of unfavorable outcome in elderly aSAH patients after endovascular coiling: A retrospective study.Frontiers in neuroscience · 2022Article
- Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling.Frontiers in neurology · 2022Article
- An accurate prognostic prediction for aneurysmal subarachnoid hemorrhage dedicated to patients after endovascular treatment.Therapeutic advances in neurological disorders · 2022Article
- A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges.Current neuropharmacology · 2022Review
- Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery.World journal of clinical cases · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 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.