This talk will give a brief introduction to OMUSE, the Oceanographic Multipurpose Software Environment, which is currently being developed. OMUSE is a Python framework that provides high-level object-oriented interfaces to existing or newly developed numerical ocean simulation codes, simplifying their use and development In this way, OMUSE facilitates the efficient design of numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales, for example coupling a global open ocean simulation with a regional coastal ocean model. OMUSE enables its users to write high-level Python scripts that describe simulations. The functionality provided by OMUSE takes care of the low-level integration with the code and deploying simulations on high-performance computing resources, allowing its users to focus on the physics of the simulation. We give an overview of the design of OMUSE and the modules and model components currently included. In particular, we will discuss the process of creating a new OMUSE interface to an existing code, and explain how OMUSE keeps track of the internal state of a running simulation. In addition, we will discuss the grid data types and grid remapping functionality that OMUSE provides. We also give an example of performing online data analysis on a running simulation, which is becoming increasingly important as models simulate a broader range of scales, generating large datasets that cannot be fully stored for offline analysis.@en

Aims Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.@en