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Clinical teaching visits (CTVs) are formative workplace-based assessments that involve a senior general practitioner (GP) observing a clinical practice session of a general practice registrar (specialist vocational GP trainee). These visits constitute a key part of Australian GP training. Despite being mandatory and resource-intensive, there is a paucity of evidence regarding the content and educational utility of CTVs. This study aims to establish the content and educational utility of CTVs across varying practice settings within Australia, as perceived by registrars and their assessors ('CT visitors'). In addition, this study aims to establish registrar, CT visitor and practice factors associated with CTV content and perceived CTV utility ratings.

This study will collect data prospectively using online questionnaires completed soon after incident CTVs. Participants will be registrars and CT visitors of CTVs conducted from March 2020 to January 2021. The setting is three Regional Training Organisations across four Australian states and territories (encompassing 37% of Australian GP registrars).Outcome factors will be a number of specified CTV content elements occurring during the CTV as well as participants' perceptions of CTV utility, which will be analysed using univariate and multivariable regression.

Ethics approval has been granted by the University of Newcastle Human Research Ethics Committee, approval number H-2020-0037. this website Study findings are planned to be disseminated via conference presentation, peer-reviewed journals, educational practice translational workshops and the GP Synergy research subwebsite.

Ethics approval has been granted by the University of Newcastle Human Research Ethics Committee, approval number H-2020-0037. Study findings are planned to be disseminated via conference presentation, peer-reviewed journals, educational practice translational workshops and the GP Synergy research subwebsite.

To translate and adapt the Chelsea Critical Care Physical Assessment Tool (CPAx) into Chinese version ('CPAx-Chi'), test the reliability and validity of CPAx-Chi, and verify the cut-off point for the diagnosis of intensive care unit-acquired weakness (ICU-AW).

Cross-sectional observational study.

Forward and back translation, cross-cultural adaptation and pretesting of CPAx into CPAx-Chi were based on the Brislin model. Participants were recruited from the general ICU of five third-grade class-A hospitals in western China. Two hundred critically ill adult patients (median age 53 years; 64% men) with duration of ICU stay ≥48 hours and Glasgow Coma Scale ≥11 were included in this study. Two researchers simultaneously and independently assessed eligible patients using the Medical Research Council Muscle Score (MRC-Score) and CPAx-Chi.

The content validity index of items was 0.889. The content validity index of scale was 0.955. Taking the MRC-Score scale as standard, the criterion validity of CPAx-Chi wastrated content validity, criterion-related validity and reliability. CPAx-Chi showed the best accuracy in assessment of patients at risk of ICU-AW with good sensitivity and specificity at a recommended cut-off of 31.

The aim was to use routine data available at a patient's admission to the hospital to predict polypharmacy and drug-drug interactions (DDI) and to evaluate the prediction performance with regard to its usefulness to support the efficient management of benefits and risks of drug prescriptions.

Retrospective, longitudinal study.

We used data from a large multicentred pharmacovigilance project carried out in eight psychiatric hospitals in Hesse, Germany.

Inpatient episodes consecutively discharged between 1 October 2017 and 30 September 2018 (year 1) or 1 January 2019 and 31 December 2019 (year 2).

The proportion of rightly classified hospital episodes.

We used gradient boosting to predict respective outcomes. We tested the performance of our final models in unseen patients from another calendar year and separated the study sites used for training from the study sites used for performance testing.

A total of 53 909 episodes were included in the study. The models' performance, as measured by the area under the receiver operating characteristic, was 'excellent' (0.83) and 'acceptable' (0.72) compared with common benchmarks for the prediction of polypharmacy and DDI, respectively. Both models were substantially better than a naive prediction based solely on basic diagnostic grouping.

This study has shown that polypharmacy and DDI can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established.

This study has shown that polypharmacy and DDI can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established.

To develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.

A nationally representative retrospective study.

Representative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.

Representative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.

In-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).

A total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as 'reference model', was 0.