ArticleJournal of neural transmission (Vienna, Austria : 1996)2012
Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm.
Article in Journal of neural transmission (Vienna, Austria : 1996), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 23 citations in OpenAlex.
- Characterization of alcohol-related seizures in withdrawal syndrome.Epilepsia open · 2024Article
- Prediction for cardiovascular diseases based on laboratory data: An analysis of random forest model.Journal of clinical laboratory analysis · 2020Article
- Alcohol withdrawal syndrome: mechanisms, manifestations, and management.Acta neurologica Scandinavica · 2017Review
- Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations.Computational and mathematical methods in medicine · 2017Review
- Identifying small groups of foods that can predict achievement of key dietary recommendations: data mining of the UK National Diet and Nutrition Survey, 2008-12.Public health nutrition · 2016Article
- Identifying binge drinkers based on parenting dimensions and alcohol-specific parenting practices: building classifiers on adolescent-parent paired data.BMC public health · 2015Article
- Health behaviors among people with epilepsy--results from the 2010 National Health Interview Survey.Epilepsy & behavior : E&B · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alcohol-withdrawal seizures (AWS) are an important and relevant complication during detoxification in alcohol-dependent patients. Therefore, it is important to evaluate the individual risk for AWS. We apply a random forest algorithm to assess possible predictive markers in a large sample of 200 alcohol-dependent patients undergoing alcohol withdrawal. This analysis showed that the combination of homocysteine, prolactin, blood alcohol concentration on admission, number of preceding withdrawals, age and the number of cigarettes smoked may successfully predict AWS. In conclusion, the results of this analysis allow for origination of further research, which should include additional biological and psychosocial parameters as well as consumption behaviour.
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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.