Mutual Information Score Research Articles

Comparison of gene-by-gene and genome-wide short nucleotide sequence-based approaches to define the global population structure of Streptococcus pneumoniae.

Defining the population structure of a pathogen is a key part of epidemiology, as genomically related isolates are likely to share key clinical features such as antimicrobial resistance profiles and invasiveness. Multiple different methods are currently used to cluster together closely related genomes, potentially leading to inconsistency between studies. Here, we use a global dataset of 26 306 Streptococcus pneumoniae genomes to compare four clustering methods: gene-by-gene seven-locus MLST, core genome MLST (cgMLST)-based hierarchical clustering (HierCC) assignments, life identification number (LIN) barcoding and k-mer-based PopPUNK clustering (known as GPSCs in this species). We compare the clustering results with phylogenetic and pan-genome analyses to assess their relationship with genome diversity and evolution, as we would expect a good clustering method to form a single monophyletic cluster that has high within-cluster similarity of genomic content. We show that the four methods are generally able to accurately reflect the population structure based on these metrics and that the methods were broadly consistent with each other. We investigated further to study the discrepancies in clusters. The greatest concordance was seen between LIN barcoding and HierCC (adjusted mutual information score=0.950), which was expected given that both methods utilize cgMLST, but have different methods for defining an individual cluster and different core genome schema. However, the existence of differences between the two methods shows that the selection of a core genome schema can introduce inconsistencies between studies. GPSC and HierCC assignments were also highly concordant (AMI=0.946), showing that k-mer-based methods which use the whole genome and do not require the careful selection of a core genome schema are just as effective at representing the population structure. Additionally, where there were differences in clustering between these methods, this could be explained by differences in the accessory genome that were not identified in cgMLST. We conclude that for S. pneumoniae, standardized and stable nomenclature is important as the number of genomes available expands. Furthermore, the research community should transition away from seven-locus MLST, whilst cgMLST, GPSC and LIN assignments should be used more widely. However, to allow for easy comparison between studies and to make previous literature relevant, the reporting of multiple clustering names should be standardized within the research.

The Impact of Lexical Bundle Length on L2 Oral Proficiency

Lexical bundles (LBs) are crucial in L2 oral proficiency, yet their complexity in terms of length is under-researched. This study therefore examines the relationship between longer and shorter LBs and oral proficiency among 150 L2 learners of varying proficiency levels at a UK university. Through the analysis of oral presentation data (scores ranging from intermediate to advanced) and employing a combined text-internal and text-external approach (two- to five-word bundles), this study advances an innovative text-internal LB refinement procedure, thus isolating the unique contribution of LB length. Robust regression, dominance analysis, and random forest statistical techniques reveal the predictive power of bigram mutual information (MI) and longer three-to-five-word sequences on higher proficiency scores. Our results show that learners using higher MI score bigrams tend to perform better in their presentations, with a strong positive impact on scores (b = 14.38, 95% CI [8.01, 20.76], t = 4.42; dominance weight = 58.63%). Additionally, the use of longer three-to-five-word phrases also contributes to better performance, though to a lesser extent (dominance weight = 18.80%). These findings highlight the pedagogical potential of a nuanced approach to the strategic deployment of LBs, particularly bigram MI, to foster oral proficiency. Suggestions for future LB proficiency research are discussed in relation to L2 speech production models.

Evaluating NLP models with written and spoken L2 samples

The use of natural language processing tools such as part-of-speech taggers and syntactic parsers are increasingly being used in studies of second language (L2) proficiency and development. However, relatively little work has focused on reporting on the accuracy of these tools or optimizing their performance in L2 contexts. While some studies reference the published overall accuracy of a particular tool or include a small-scale accuracy analysis, very few (if any) studies provide a comprehensive account of the performance of taggers and parsers across a range of written and spoken registers. In this study, we provide a large-scale accuracy analysis of popular taggers and parsers across L1 and L2 written and spoken texts, both when default and L2-optimized models are used. Accuracy is examined both at the feature level (e.g., identifying adjective-noun relationships) and the text level (e.g., mean mutualinformation scores). The results highlight the strength and weaknesses of these tools.

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.

Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.

Phenotypic clustering of bipolar disorder supports stratification by lithium responsiveness over diagnostic subtypes.

The aim of this study was to determine whether the clinical profiles of bipolar disorder (BD) patients could be differentiated more clearly using the existing classification by diagnostic subtype or by lithium treatment responsiveness. We included adult patients with BD-I or II (N = 477 across four sites) who were treated with lithium as their principal mood stabilizer for at least 1 year. Treatment responsiveness was defined using the dichotomized Alda score. We performed hierarchical clustering on phenotypes defined by 40 features, covering demographics, clinical course, family history, suicide behaviour, and comorbid conditions. We then measured the amount of information that inferred clusters carried about (A) BD subtype and (B) lithium responsiveness using adjusted mutual information (AMI) scores. Detailed phenotypic profiles across clusters were then evaluated with univariate comparisons. Two clusters were identified (n = 56 and n = 421), which captured significantly more information about lithium responsiveness (AMI range: 0.033 to 0.133) than BD subtype (AMI: 0.004 to 0.011). The smaller cluster had disproportionately more lithium responders (n = 47 [83.8%]) when compared to the larger cluster (103 [24.4%]; p = 0.006). Phenotypes derived from detailed clinical data may carry more information about lithium responsiveness than the current classification of diagnostic subtype. These findings support lithium responsiveness as a valid approach to stratification in clinical samples.

Image clustering using generated text centroids

In recent years, deep neural networks pretrained on large-scale datasets have been used to address data deficiency and achieve better performance through prior knowledge. Contrastive language–image pretraining (CLIP), a vision-language model pretrained on an extensive dataset, achieves better performance in image recognition. In this study, we harness the power of multimodality in image clustering tasks, shifting from a single modality to a multimodal framework using the describability property of image encoder of the CLIP model. The importance of this shift lies in the ability of multimodality to provide richer feature representations. By generating text centroids corresponding to image features, we effectively create a common descriptive language for each cluster. It generates text centroids assigned by the image features and improves the clustering performance. The text centroids use the results generated by using the standard clustering algorithm as a pseudo-label and learn a common description of each cluster. Finally, only text centroids were added when the image features on the same space were assigned to the text centroids, but the clustering performance improved significantly compared to the standard clustering algorithm, especially on complex datasets. When the proposed method is applied, the normalized mutual information score rises by 32% on the Stanford40 dataset and 64% on ImageNet-Dog compared to the k-means clustering algorithm.

Utilizing Machine Learning to Forecast Smoking Behavior

Abstract: The examination of smoking habits, extensively studied over time, has posed challenges in accurately predicting and thoroughly analyzing its determinants. Previous research efforts struggled to precisely predict smoking behavior due to the presence of continuous target variables, which hindered the application of crucial feature selection techniques like mutual information. This study aims to tackle these hurdles through an innovative approach that integrates data preprocessing, feature engineering, and advanced machine learning methods. To address the issue of continuous target variables, our methodology involves categorizing smoking behavior into discrete groups, enabling the utilization of feature selection techniques such as mutual information scores. Logistic regression, Gaussian Naive Bayes, and Random Forest Classifier models are utilized to achieve highly accurate predictions of smoking behavior. The Select KBest method is employed to evaluate the importance of features based on mutual information scores. The investigation delves into various health markers, including BMI, haemoglobin levels, and cholesterol, offering comprehensive insights into their influence on smoking habits. Furthermore, Principal Component Analysis (PCA) is implemented to reduce dimensionality while preserving essential information from the dataset. Through this novel approach and a steadfast commitment to ethical data collection practices, our objective is to advance the comprehension of smoking behavior, surmounting past challenges, and providing valuable insights for public health initiatives and smoking cessation endeavors. The paper assesses outcomes using specified algorithms and parameters, presenting a comparative analysis to enhance the clarity and reliability of our findings.

A Corpus-based Ecosophical Analysis of Discourse Produced Around the China-Pakistan Economic Corridor (CPEC)

This research explores the Ecosophy of discourse produced around the China-Pakistan Economic Corridor (CPEC). The study starts with building a corpus of texts, taken from the official website of the CPEC. The list of 18 key words was made, based on three United Nations (UN) Sustainable Development Goals (SDGs) out of 17 goals. Theoretical framework of Stibbe’s (2015) ecocritical discourse perspective is used to develop the Ecosophy of the CPEC, while analytical framework is based on three UN sustainable development goals outlined in 2015. Corpus linguistics as a methodology is used to undergo quantitative and qualitative analysis. In quantitative analysis, collocates were analyzed by identifying Mutual Information (MI) score. The highest MI score (11.26013) is gained by the collocation named CPEC-Deforestation and the lowest is of CPEC-Water (0.87352) which show that the infrastructure project of the CPEC caused deforestation and less affected water.

A Corpus-Based Analysis of Lexical Collocation in Conversation Texts of English Textbooks

This study revealed the analysis of lexical collocation in conversation texts of curriculum-based English textbook entitled “Bahasa Inggris” for tenth- grade students. In the textbook, it was found that there were 46 lexical collocations of VN, AdjN, NN, AdvAdj, and VAdv combination. Five out of seven types of lexical collocation combinations were presents in the conversation texts. The MI (Mutual Information) score of the collocation combinations were mostly above 3.00 which considered ‘strong collocation’. Semantically, the collocations were dominated by ‘free combination’ followed by ‘restricted collocation’ then figurative idiom’. The result also showed the lack of repetition of collocation combination in the conversation texts. Moreover, there was an absence of ‘pure idioms’ as one of the part that construct a conversationKey Words: conversation, corpus, lexical collocation

A Contrastive Corpus-based Study of American and English Adjectives: ‘Important’ and ‘Significant’

This contrastive corpus-based study aims to analyze the similarities and differences of two synonyms “important” and “significant” concerning on the degree of formality in their distribution across genres as well as their collocations and semantic preference. The corpus data derived from the Corpus of Contemporary American English (COCA) and the British National Corpus (BNC) which represent varieties of English usage across different cultures (American and British). This study analyzed the top 20 noun collocates with Mutual Information (MI) scores value of at least three. The corpus data from COCA and BNC explored some shared characteristics and contrastive features. The findings discovered that the two adjectives have the same core meaning and degree of formality since their highest frequency in academic texts whereas frequencies are the lowest in informal genres i.e. TV and movie subtitles and fiction. In certain conditions, these synonyms are different in terms of noun collocation due to the fact that these synonyms co-occur with particular noun collocates. Interestingly, the findings also confirm two adjectives are near synonyms as they share only two similar theme of semantic preference. In consequence, the two synonyms cannot be used interchangeably in all contexts. Moreover, it was found that the corpora provide some useful details that are deficient in the dictionary.

Predicting Smoking Status of a Person using Machine Learning

Abstract: The study of smoking behavior, a topic extensively researched over time, has presented difficulties in accurately predicting and thoroughly analyzing its determinants. Previous studies struggled to predict smoking behavior accurately, mainly due to the presence of continuous target variables, hindering the application of vital feature selection methods like mutual information. This research aims to address these challenges through an innovative approach that incorporates data preprocessing, feature engineering, and advanced machine learning techniques. To overcome the issue of continuous target variables, our methodology involves categorizing smoking behavior into discrete groups, allowing the use of feature selection methods such as mutual information scores. Logistic regression, Gaussian Naive Bayes, and Random Forest Classifier models are employed in this study to achieve highly accurate predictions of smoking behavior. The Select KBest method is utilized to assess the significance of features based on mutual information scores. The research explores various health indicators, including BMI, haemoglobin levels, and cholesterol, providing comprehensive insights into their impact on smoking behavior. The principal component analysis, or PCA, is another technique used to lower multiplicity. while retaining essential information from the dataset. Through this innovative approach and a rigorous commitment to ethical data collection practices, our goal is to advance the understanding of smoking behavior, overcoming previous challenges, and offering valuable insights for public health initiatives and smoking cessation efforts. The study evaluates results using specified algorithms and parameters, presenting a comparative analysis to enhance the clarity and robustness of our findings.

A comparative analysis of multiword units in the reading and listening input of English textbooks

This study examines the occurrence and function of multiword units (MWUs) found in reading and listening input within EFL textbooks commonly used at a Vietnamese university. Lists of MWUs were automatically extracted from this input based on frequency, range, and mutual information score criteria, followed by manual evaluation via a classification framework developed by Simpson-Vlach and Ellis (2010). A final merged list of English textbook MWUs which was used to compare the frequency and functions of MWUs across reading and listening input. The findings revealed significant differences in MWU occurrence and function between reading and listening input, with MWUs occurring more frequently in oral input compared with written input. High frequency spoken MWUs had higher frequency counts than their high frequency written counterparts. A wider range of MWUs were produced in written input, with fewer MWUs being repeatedly used. Reading input exhibited a notably lower presence of stance expressions when contrasted with listening input, while special conversational functions featured more prominently in listening input. The findings suggest EFL teachers and material developers should carefully consider the presence and functions of MWUs found in textbooks, and whether textbook input should be supplemented with other suitable input forms to help learners improve learners’ knowledge and use of MWUs.

On preserving anatomical detail in statistical shape analysis for clustering: focus on left atrial appendage morphology.

Statistical shape analysis (SSA) with clustering is often used to objectively define and categorise anatomical shape variations. However, studies until now have often focused on simplified anatomical reconstructions, despite the complexity of studied anatomies. This work aims to provide insights on the anatomical detail preservation required for SSA of highly diverse and complex anatomies, with particular focus on the left atrial appendage (LAA). This anatomical region is clinically relevant as the location of almost all left atrial thrombi forming during atrial fibrillation (AF). Moreover, its highly patient-specific complex architecture makes its clinical classification especially subjective. Preliminary LAA meshes were automatically detected after robust image selection and wider left atrial segmentation. Following registration, four additional LAA mesh datasets were created as reductions of the preliminary dataset, with surface reconstruction based on reduced sample point densities. Utilising SSA model parameters determined to optimally represent the preliminary dataset, SSA model performance for the four simplified datasets was calculated. A representative simplified dataset was selected, and clustering analysis and performance were evaluated (compared to clinical labels) between the original trabeculated LAA anatomy and the representative simplification. As expected, simplified anatomies have better SSA evaluation scores (compactness, specificity and generalisation), corresponding to simpler LAA shape representation. However, oversimplification of shapes may noticeably affect 3D model output due to differences in geometric correspondence. Furthermore, even minor simplification may affect LAA shape clustering, where the adjusted mutual information (AMI) score of the clustered trabeculated dataset was 0.67, in comparison to 0.12 for the simplified dataset. This study suggests that greater anatomical preservation for complex and diverse LAA morphologies, currently neglected, may be more useful for shape categorisation via clustering analyses.

Framework for Deep Learning Based Multi-Modality Image Registration of Snapshot and Pathology Images.

Multi-modality image registration is an important task in medical imaging because it allows for information from different domains to be correlated. Histopathology plays a crucial role in oncologic surgery as it is the gold standard for investigating tissue composition from surgically excised specimens. Research studies are increasingly focused on registering medical imaging modalities such as white light cameras, magnetic resonance imaging, computed tomography, and ultrasound to pathology images. The main challenge in registration tasks involving pathology images comes from addressing the considerable amount of deformation present. This work provides a framework for deep learning-based multi-modality registration of microscopic pathology images to another imaging modality. The proposed framework is validated on the registration of prostate ex-vivo white light camera snapshot images to pathology hematoxylin-eosin images of the same specimen. A pipeline is presented detailing data acquisition, protocol considerations, image dissimilarity, training experiments, and validation. A comprehensive analysis is done on the impact of pre-processing, data augmentation, loss functions, and regularization. This analysis is supplemented by clinically motivated evaluation metrics to avoid the pitfalls of only using ubiquitous image comparison metrics. Consequently, a robust training configuration capable of performing the desired registration task is found. Utilizing the proposed approach, we achieved a dice similarity coefficient of 0.96, a mutual information score of 0.54, a target registration error of 2.4 mm, and a regional dice similarity coefficient of 0.70.

Feature redundancy removal for text classification using correlated feature subsets

AbstractThe curse of high dimensionality in text classification is a worrisome problem that requires efficient and optimal feature selection (FS) methods to improve classification accuracy and reduce learning time. Existing filter‐based FS methods evaluate features independently of other related ones, which can then lead to selecting a large number of redundant features, especially in high‐dimensional datasets, resulting in more learning time and less classification performance, whereas information theory‐based methods aim to maximize feature dependency with the class variable and minimize its redundancy for all selected features, which gradually becomes impractical when increasing the feature space. To overcome the time complexity issue of information theory‐based methods while taking into account the redundancy issue, in this article, we propose a new feature selection method for text classification termed correlation‐based redundancy removal, which aims to minimize the redundancy using subsets of features having close mutual information scores without sequentially seeking already selected features. The idea is that it is not important to assess the redundancy of a dominant feature having high classification information with another irrelevant feature having low classification information and vice‐versa since they are implicitly weakly correlated. Our method, tested on seven datasets using both traditional classifiers (Naive Bayes and support vector machines) and deep learning models (long short‐term memory and convolutional neural networks), demonstrated strong performance by reducing redundancy and improving classification compared to ten competitive metrics.

Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.

The advancement of single-cell sequencing technology has smoothed the ability to do biological studies at the cellular level. Nevertheless, single-cell RNA sequencing (scRNA-seq) data presents several obstacles due to the considerable heterogeneity, sparsity and complexity. Although many machine-learning models have been devised to tackle these difficulties, there is still a need to enhance their efficiency and accuracy. Current deep learning methods often fail to fully exploit the intrinsic interconnections within cells, resulting in unsatisfactory results. Given these obstacles, we propose a unique approach for analyzing scRNA-seq data called scMPN. This methodology integrates multi-layer perceptron and graph neural network, including attention network, to execute gene imputation and cell clustering tasks. In order to evaluate the gene imputation performance of scMPN, several metrics like cosine similarity, median L1 distance and root mean square error are used. These metrics are utilized to compare the efficacy of scMPN with other existing approaches. This research utilizes criteria such as adjusted mutual information, normalized mutual information and integrity score to assess the efficacy of cell clustering across different approaches. The superiority of scMPN over current single-cell data processing techniques in cell clustering and gene imputation investigations is shown by the experimental findings obtained from four datasets with gold-standard cell labels. This observation demonstrates the efficacy of our suggested methodology in using deep learning methodologies to enhance the interpretation of scRNA-seq data.

K- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm.

Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .

Categorization of Emotion Based on Causality

Background: Emotions come in all shapes and forms. Some of them can be external, visible, and clearly comprehensible, while others can seemingly be coming out of thin air. Knowing what causes an emotion is crucial for better therapy and mental health. Therefore, in this manuscript, we address the problem of emotions causality. Methods: We propose a comparison of three traditional clustering models: Gaussian mixture model, HDBSCAN, and fuzzy c-means, to categorize each emotion described in the DEAP database. It contains over 1700 points, and has no prior label as to which type of stressor the subject’s emotion is generated from. This labelling task has been conducted by a psychiatrist. Results: The fuzzy c-means yields the highest results, with an accuracy of 57.13%, followed by the Gaussian mixture model at 39.49% and the HDBSCAN method with only 18.86%. Another score computed is the mutual information score which shows how homogenous the clusters are for each model. Conclusion: The data from DEAP is very heterogeneous and its density is stable, which may indicate that classification would be the better option, in terms of accuracy and homogeneity of the clusters.

ScAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.

Workforce diversity in specialist physicians: Implications of findings for religious affiliation in Anaesthesia & Intensive Care.

Minority ethnic identification between physician and patient can reduce communication and access barriers, improve physician-patient relationship, trust, and health outcomes. Religion influences health beliefs, behaviours, treatment decisions, and outcomes. Ethically contentious dilemmas in treatment decisions are often entangled with religious beliefs. They feature more in medical specialties such as Anaesthesia & Intensive Care, with issues including informed consent for surgery, organ donation, transplant, transfusion, and end-of-life decisions. We investigate diversity in religious affiliation in the UK medical workforce, using data from the General Medical Council (GMC) specialist register and Health Education England (HEE) trainee applications to medical specialties. We performed conservative Bonferroni corrections for multiple comparisons using Chi-squared tests, as well as normalised mutual-information scores. Robust associations that persisted on all sensitivity analyses are reported, investigating whether ethnicity or foreign primary medical qualification could explain the underlying association. The only significant and robust association in both GMC and HEE datasets affecting the same religious group and specialty was disproportionately fewer Anaesthesia & Intensive Care physicians with a religious affiliation of "Muslim", both as consultants (RR 0.57[0.47,0.7]) and trainee applicants (RR 0.27[0.19,0.38]. Associations were not explained by ethnicity or foreign training. We discuss the myriad of implications of the findings for multi-cultural societies. Lack of physician workforce diversity has far-reaching consequences, especially for specialties such as Anaesthesia and Intensive Care, where ethically contentious decisions could have a big impact. Religious beliefs and practices, or lack thereof, may have unmeasured influences on clinical decisions and on whether patients identify with physicians, which in turn can affect health outcomes. Examining an influencing variable such as religion in healthcare decisions should be prioritised, especially considering findings from the clinician-patient concordance literature. It is important to further explore potential historical and socio-cultural barriers to entry of training medics into under-represented specialties, such as Anaesthesia and Intensive Care.