ArticleJournal of neurological surgery. Part B, Skull base2018
Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.
Article in Journal of neurological surgery. Part B, Skull base, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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, 1 synthesis or guideline pooled it.
- Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.Acta neurochirurgica · 2024Pooled it
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
- Artificial intelligence innovations in neurosurgical oncology: a narrative review.Journal of neuro-oncology · 2024Review
- Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.PloS one · 2024Article
- Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department.Journal of personalized medicine · 2022Article
- Machine Learning Models for Predicting Postoperative Outcomes following Skull Base Meningioma Surgery.Journal of neurological surgery. Part B, Skull base · 2022Review
- Early prediction of patient discharge disposition in acute neurological care using machine learning.BMC health services research · 2022Article
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units.Healthcare (Basel, Switzerland) · 2022Article
- A systematic review of the prediction of hospital length of stay: Towards a unified framework.PLOS digital health · 2022Article
- Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.PloS one · 2022Article
- Prediction of Critical Care Outcome for Adult Patients Presenting to Emergency Department Using Initial Triage Information: An XGBoost Algorithm Analysis.JMIR medical informatics · 2021Article
- Application of the Preoperative Assistant System Based on Machine Learning in Hepatocellular Carcinoma Resection.Journal of healthcare engineering · 2021Article
- Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.Internal and emergency medicine · 2020Article
- Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance.Neurosurgery · 2019Article
- Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning.Nuclear medicine and molecular imaging · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.