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This paper presents a new model, the subsequent injury categorisation (SIC) model, for categorising subsequent sports injuries that takes into account the need to include both acute and overuse injuries and ten different dependency structures between injury types.
The aims of this paper are therefore to present a new model for categorising subsequent injuries in the sports injury context.
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In brief, we propose a method for categorising continuous predictors in clinical prediction models, which includes both the number of categories and the best cut points.
The method for categorising continuous covariates was evaluated, by comparing the performance of the proposed categorical predictor in the model to that of the original continuous variable modelled by a GAM as the best option in the same model.
are inadequate for categorising the factors that can be present in a single experiment.
We also present a taxonomy for categorising possible architectures according to their main attributes.
For this a method is necessary for categorising high-speed tracks with respect to their wind exposure.
The normalization process model assisted in categorising the challenges experienced during implementation of the intervention.
Landsat-Enhanced Thematic Mapper Plus (ETM+) data are applied to a classification and regression tree (CART) model to categorise land cover types for the urban areas of interest.
The model correctly categorised 57% of the participants.
Typical criteria for categorising models are top-down (macro-economic relationship of components) vs. bottom-up (technology specific) approaches, simulation vs. optimisation of the system, and partial equilibrium (e.g. considering only the power sector) vs. equilibrium (considering the whole economy) models [20].
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CEO of Professional Science Editing for Scientists @ prosciediting.com