Claw horn disruption lesions (CHDLs) in dairy cattle account for a large proportion of lameness. The aim of this review is to provide an update on the evidence surrounding the pathogenesis of CHDLs, in the context of how statistical modelling has contributed to the validity of available evidence and current thinking. Historically, 'subclinical laminitis' has often been used to describe the commonly accepted underlying pathology associated with these lesions, however progress in understanding the aetiopathogenesis of CHDLs and a lack of clear evidence to support the traditional laminitis hypothesis, means use of this terminology has been challenged. With advancements in statistical modelling capabilities within the veterinary field, the multifactorial and complex nature of CHDLs can be more fully explored. This has led to an increased understanding of environmental and animal-based risk factors and their role in the pathogenesis of CHDLs, as well as highlighting future research areas. There is still a need for further research using intervention studies to demonstrate causality for identified risk factors to date, as well as quantifying the impact of these risk factors at the population level. Some important considerations when using and interpreting statistical models in lameness research are discussed with a critical assessment of the key statistical issues in published research investigating the pathogenesis of CHDLs.
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http://dx.doi.org/10.1016/j.tvjl.2018.07.002 | DOI Listing |
Surv Methodol
December 2024
Department of Statistical Science, 214a Old Chemistry Building, Duke University, Durham, NC 27708-0251.
When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993).
View Article and Find Full Text PDFInt J Public Health
January 2025
Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Objectives: Research questions about how and why health trends differ between populations require decisions about data analytic procedure. The objective was to document and compare the information returned from stratified, fixed effect and random effect approaches to data modelling for two prototypical descriptive research questions about comparative trends in toothbrushing.
Methods: Data included five cycles of the Health Behaviour in School-aged Children 2006 to 2022, which provided a sample of 980192 11- to 15- year olds from 35 countries.
Pak J Med Sci
January 2025
Lianghui Zheng Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics, Gynecology and Pediatrics, Fujian Medical University. P.R. China.
Objective: This retrospective cohort study aimed to investigate the effects of parity on gestational weight gain (GWG) and its association with maternal and neonatal outcomes in women with gestational diabetes mellitus (GDM).
Methods: This retrospective cohort study data from 2,909 pregnant women with GDM who delivered between 2021 and 2023 at Fujian Maternity and Child Health hospital, were analyzed. Participants were categorized into nulliparous (no previous births), primiparous (one previous birth), and multiparous (two or more previous births) groups.
Water Res X
May 2025
Institute for Artificial Intelligence R&D of Serbia, Fruškogorska 1, Novi Sad 21000, Serbia.
This study evaluates three Machine Learning (ML) models-Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. We adopt a data-centric approach, utilizing large, validated datasets to train the models, and apply SHapley Additive exPlanations (SHAP) to enhance the interpretability and reliability of the ML models. The results show that TKAN outperforms LSTM but slightly lags behind TCN in streamflow forecasting.
View Article and Find Full Text PDFJ Intensive Care Soc
January 2025
Department of Anaesthesia, Critical Care, and Pain Medicine, Royal Infirmary of Edinburgh, University of Edinburgh, Edinburgh, UK.
Background: Identifying women at highest or lowest risk of perinatal intensive care unit (ICU) admission may enable clinicians to risk stratify women antenatally so that enhanced care or elective admission to ICU may be considered or excluded in birthing plans. We aimed to develop a statistical model to predict the risk of maternal ICU admission.
Methods: We studied 762,918 pregnancies between 2005 and 2018.
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