ArticleInfectious Disease Modelling2020
A primer on model selection using the Akaike Information Criterion.
Article in Infectious Disease Modelling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06445920 (Risk Factors Analysis for Clinical Important Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Gastrointestinal Surgery Based on LASSO and Stepwise Regression), which is not on this map. Cited by 202 papers, 1 of them a synthesis that pooled it.
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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.
Risk Factors Analysis for Clinical Important Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Gastrointestinal Surgery Based on LASSO and Stepwise Regression
Who cites it
202 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Development and validation of a prediction model for in-hospital mortality of patients with severe thrombocytopenia.Scientific reports · 2022Trial
- Multilevel growth curve analyses of behavioral activation for anhedonia (BATA) and mindfulness-based cognitive therapy effects on anhedonia and resting-state functional connectivity: Interim results of a randomized trialJournal of affective disorders · 2021Trial
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142 more citing papers are in PubMed but not listed here.
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1 author.
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Abstract
A powerful investigative tool in biology is to consider not a single mathematical model but a collection of models designed to explore different working hypotheses and select the best model in that collection. In these lecture notes, the usual workflow of the use of mathematical models to investigate a biological problem is described and the use of a collection of model is motivated. Models depend on parameters that must be estimated using observations; and when a collection of models is considered, the best model has then to be identified based on available observations. Hence, model calibration and selection, which are intrinsically linked, are essential steps of the workflow. Here, some procedures for model calibration and a criterion, the Akaike Information Criterion, of model selection based on experimental data are described. Rough derivation, practical technique of computation and use of this criterion are detailed.
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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.