Continuous Predictors Research Articles

Blouch: Bayesian Linear Ornstein-Uhlenbeck Models for Comparative Hypotheses.

Relationships among species in the tree of life can complicate comparative methods and testing adaptive hypotheses. Models based on the Ornstein-Uhlenbeck process permit hypotheses about adaptation to be tested by allowing traits to either evolve towards fixed adaptive optima (e.g., regimes or niches) or track continuously changing optima that can be influenced by other traits. These models allow estimation of the effects of both adaptation and phylogenetic inertia - resistance to adaptation due to any source - on trait evolution, an approach known as the "adaptation-inertia" framework. However, previous applications of this framework, and most approaches suggested to deal with the issue of species non-independence, are based on a maximum likelihood approach and thus it is difficult to include information based on prior biological knowledge in the analysis, which can affect resulting inferences. Here I present Blouch, (Bayesian Linear Ornstein-Uhlenbeck Models for Comparative Hypotheses), which fits allometric and adaptive models of continuous trait evolution in a Bayesian framework based on fixed or continuous predictors and incorporates measurement error. I first briefly discuss the models implemented in Blouch, and then the new applications for these models provided by a Bayesian framework. This includes the advantages of assigning biologically meaningful priors when compared to non-Bayesian approaches, allowing for varying effects (intercepts and slopes), and multilevel modeling. Validations on simulated data show good performance in recovering the true evolutionary parameters for all models. To demonstrate the workflow of Blouch on an empirical dataset, I test the hypothesis that the relatively larger antlers of larger bodied deer are the result of more intense sexual selection that comes along with their tendency to live in larger breeding groups. While results show that larger bodied deer that live in larger breeding groups have relatively larger antlers, deer living in the smallest groups appear to have a different and steeper scaling pattern of antler size to body size than other groups. These results are contrary to previous findings and may argue that a different type of sexual selection or other selective pressures govern optimum antler size in the smallest breeding groups.

Baseline Econometric Model and Solar Energy Requirement for Potential Hydrogen Demand in Tarlac, Philippines

This study aims to build a baseline econometric model for the possible development of green hydrogen production in Tarlac. Scenario analysis will consider hydrogen produced from renewable solar energy as an alternative energy carrier in the fuel mix for electricity generation in Tarlac to achieve sustainable economic growth and improve energy security while minimizing carbon emissions and improving resource efficiency. Tarlac is the chosen area to be considered in this study because this is where New Clark City is being established. New Clark City is a 9450-hectare disaster-resilient, environment-friendly, integrated urban development metropolis. It is important to build the baseline econometric model before proceeding with scenario generation. The parameters used are determined from available historical data through statistical procedures. The basic assumption is that the relationships between the independent and dependent variables that existed in the past will continue to be true in the future. The energy demand data and the continuous predictors were used to build a baseline econometric model for potential hydrogen demand in Tarlac, Philippines. Model equations were obtained using stepwise regression analysis. These model equations were used to forecast the energy demand up to 2032. These forecasted energy demands will be used in scenario generation to integrate green hydrogen production. Preliminary assessments of green hydrogen scenarios that are based on the assumption of renewable energy surplus generated from solar power plants in Tarlac are discussed. The forecasted demand will be used to assess the scenario generation in integrating green hydrogen in New Clark City. The proposed energy management strategy of storing surplus renewable energy, such as green hydrogen, for the power sector has the potential to impact various stakeholders, including industries and the government, significantly. This strategy aligns with transitioning to cleaner and more sustainable energy sources, contributing to national and international environmental targets. Adopting green hydrogen storage can contribute to energy security by diversifying the sources of power generation.

Variable importance based interaction modelling with an application on initial spread of COVID-19 in China

Abstract Interaction selection for linear regression is useful in many fields of modern science, yet very challenging. Existing methods focus on finding one optimal model but they may perform poorly in terms of stability for high-dimensional data, and they do not typically deal with categorical predictors. In this paper, we introduce a variable importance based interaction modelling (VIBIM) procedure for learning interactions in a linear regression model with both continuous and categorical predictors. We apply the VIBIM procedure to a COVID-19 data and show that the VIBIM approach leads to better models in terms of interpretability, stability, reliability, and prediction.

Pointwise Nonparametric Estimation of Odds Ratio Curves with R: Introducing the flexOR Package

The analysis of odds ratio curves is a valuable tool in understanding the relationship between continuous predictors and binary outcomes. Traditional parametric regression approaches often assume specific functional forms, limiting their flexibility and applicability to complex data. To address this limitation and introduce more flexibility, several smoothing methods may be applied, and approaches based on splines are the most frequently considered in this context. To better understand the effects that each continuous covariate has on the outcome, results can be expressed in terms of splines-based odds ratio (OR) curves, taking a specific covariate value as reference. In this paper, we introduce an R package, flexOR, which provides a comprehensive framework for pointwise nonparametric estimation of odds ratio curves for continuous predictors. The package can be used to estimate odds ratio curves without imposing rigid assumptions about their underlying functional form while considering a reference value for the continuous covariate. The package offers various options for automatically choosing the degrees of freedom in multivariable models. It also includes visualization functions to aid in the interpretation and presentation of the estimated odds ratio curves. flexOR offers a user-friendly interface, making it accessible to researchers and practitioners without extensive statistical backgrounds.

Timing of administration of antenatal magnesium sulphate and correlation with umbilical cord blood magnesium levels in preterm babies

BackgroundThe effectiveness of MgSO4 for foetal neuroprotection is acknowledged, but the best time to provide it in relation to birth is a conundrum, and dose schedule is yet unknown. Understanding the determinants of the magnesium levels in cord blood aids in determining the appropriate timing and length of administration. Aim and objectiveTo assess the cord blood magnesium concentration in relation to the timing of MgSO4 and delivery. To achieve ROC in relation to optimum level of cord blood magnesium concentration in relation to neonatal outcome variables. Study designA prospective observational study conducted in a tertiary care hospital over 2 years in women having preterm delivery from 26 weeks to 33 + 6 weeks, who received Neuroprophylaxis. Cord blood was collected for magnesium level estimation. Baby followed 24 h after delivery. ROC analysis performed for predicting an optimal cut-off for a continuous predictor predicting binary outcome. Results85 recruited cases divided into bolus group, bolus + infusion group. The mean cord blood magnesium (n = 85) was 3.8 mg/dl. The AUROC for Gestational Age at Administration predicting Baby Outcome: 0.699, It was statistically significant (p = 0.034). The AUROC for Cord Blood Mg predicting Baby Outcome: 0.606, It was not statistically significant (p = 0.262). ConclusionMean cord blood magnesium levels served as a tool to determine the timing and duration of Neuroprophylaxis. Mean cord blood magnesium of 3.8 mg/dl should be achieved to serve the purpose of Neuroprotection. To achieve this, Bolus followed by Infusion should be administered for at-least 6 h prior to delivery.

Principal simple linear regression

In this paper, we propose a new method called principal simple linear regression for predicting a continuous response variable, $Y$ using a single continuous predictor, $X$ by utilizing multiple regression lines. This method is based on the theory of principal points and offers several advantages over classical simple linear regression methods, such as the ability to predict central, dispersion, and distributional tendencies of $Y$ on $X$, and simultaneous estimation. We provide the main properties, inferences, and limiting behavior of the estimators. Additionally, we conduct a comprehensive simulation study to validate our theoretical results. The model is also applied to real datasets to demonstrate its effectiveness.

Predictive Model of Recovery to Prefracture Activities-of-Daily-Living Status One Year after Fragility Hip Fracture.

Background and Objectives: Achieving prefracture functional status is a critical objective following a hip fracture, yet fewer than half of patients reach this milestone. The adoption of tools for assessing functional outcomes is increasingly recognized as essential for evaluating recovery following treatment for fragility hip fractures. We developed multivariable clinical prediction criteria to estimate the likelihood of patients regaining their prefracture activities-of-daily-living (ADL) status one year after sustaining a fragility hip fracture. Materials and Methods: A retrospective cohort of patients treated for fragility hip fractures at a university-affiliated tertiary care center between February 2017 and April 2019 served as the basis for developing and internally validating the clinical prediction criteria. We applied a multivariable fractional polynomial method to integrate several continuous predictors into a binary logistic regression model. Results: The study included 421 patients, 324 (77%) of whom reported regaining their prefracture activities-of-daily-living level one year after experiencing fragility hip fractures. Significant predictors, such as the prefracture Barthel index, EQ-VAS score, and treatment modality, were incorporated into the predictive model. The model demonstrated excellent discriminative power (AuROC of 0.86 [95% CI 0.82-0.91]) and satisfactory calibration. Conclusions: The predictive model has significant discriminative ability with good calibration and provides clinicians with a means to forecast the recovery trajectories of individual patients one year after a fragility hip fracture, which could be useful because prompt clinical decision-making is aided by this information. Patients and caregivers can also be counseled and encouraged to follow up with the medical activities and interventions deemed essential by doctors who used the prediction tool. Access to the model is provided through a web application. External validation is warranted in order to prove its applicability and generalizability.

Effectiveness of workplace choice architecture modification for healthy eating and daily physical activity

BackgroundModifying the choice architecture of behavioural contexts can facilitate health behaviour change, but existing evidence builds mostly on small-scale interventions limited in duration, targets, strategies, and settings. We evaluated the effectiveness of a one-year hybrid type 2 implementation-effectiveness trial aimed at promoting healthy eating and daily physical activity with subtle modifications to the choice architecture of heterogeneous worksites. The intervention was contextualised to and integrated into the routine operations of each worksite. Effectiveness was evaluated in a quasi-experimental pre-post design.MethodsIntervention sites (n = 21) implemented a median of two (range 1–9) intervention strategies for healthy eating and one (range 1–5) for physical activity. Questionnaires pre (n = 1126) and post (n = 943) intervention surveyed employees’ behavioural patterns at work (food consumption: vegetables/roots, fruit/berries, nuts/almonds/seeds, sweet treats, fast food, water; physical activity: restorative movement, exercise equipment use, stair use). The post-intervention questionnaire also measured employees’ perception of and response to three intervention strategies: a packed lunch recipe campaign, a fruit crew-strategy, and movement prompts. Multi- and single-level regression models evaluated effectiveness, treating intervention as a continuous predictor formed of the site-specific dose (n intervention strategies employed) and mean quality (three-point rating per strategy halfway and at the end of the intervention) of implementation relevant to each outcome.ResultsMultinomial logistic regression models found the intervention significantly associated with a favourable change in employees’ fruit and berry consumption (interaction effect of time and implementation p = 0.006) and with an unfavourable change in sweet treat consumption (p = 0.048). The evidence was strongest for the finding concerning fruit/berry consumption—an outcome that sites with greater dose and quality of implementation targeted by using strategies that reduced the physical effort required to have fruit/berries at work and by covering multiple eating-related contexts at the worksite. The quality of implementation was positively associated with the perception of (p = 0.044) and response to (p = 0.017) the packed lunch recipes, and with response to the fruit crew-strategy (p < 0.001).ConclusionsThe results suggest that a contextualised, multicomponent choice architecture intervention can positively influence eating behaviour in diverse real-world settings over a one-year period, and that higher implementation quality can enhance intervention perception and response. However, outcomes may depend on the type of intervention strategies used and the extent of their delivery.

Continuity of mental disorders in children with chronic physical illness.

Data on the chronicity of mental disorder in children with chronic physical illness (CPI) are limited. We examined the prevalence and predictors of homotypic and heterotypic continuity of mental disorder in children with CPI. A sample of 263 children aged 2-16 years with physician-diagnosed CPI were recruited from outpatient clinics (e.g., dermatology, respiratory) at a Canadian pediatric academic hospital and followed for 24 months. Parent and child-reported mental disorders (mood, anxiety, behavioral, attention-deficit hyperactivity disorder [ADHD]) were assessed using the Mini International Neuropsychiatric Interview for Children and Adolescents at baseline, 6, 12, and 24 months. Marginal regression models were computed to identify clinical, parent, and demographic factors associated with mental comorbidity over time. Mental disorder was observed in 24-27% of children with CPI based on child reports and 35-39% based on parent reports. Parent-reported models revealed significant homotypic continuity for all mental disorders (ORs = 4.2-9.5), and heterotypic continuity between mood and anxiety disorders (OR = 2.2), ADHD and behavioral disorders (OR = 5.1), and behavioral and each mental disorder (ORs = 6.7-8.4). Child-reported models revealed significant homotypic continuity for mood (OR = 8.8) and anxiety disorder (OR = 6.0), and heterotypic continuity between anxiety and mood disorders (OR = 12.4). Child disability (ORs = 1.3-1.5) and parent psychopathology (ORs = 1.2-1.8) were the most consistent predictors of both child- and parent-reported mental disorder over time. Mental comorbidity was prevalent and persistent in children with CPI with homotypic and heterotypic continuity common across informants. Child disability and parent psychopathology may be priority targets within integrated family-centered models of care to prevent mental comorbidity in children with CPI.

Study protocol for the development and validation of a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia.

Patients with dementia and their caregivers could benefit from advance care planning though may not be having these discussions in a timely manner or at all. A prognostic tool could serve as a prompt to healthcare providers to initiate advance care planning among patients and their caregivers, which could increase the receipt of care that is concordant with their goals. Existing prognostic tools have limitations. We seek to develop and validate a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia. The derivation cohort will include approximately 235,000 patients with dementia, who were admitted to hospital in Ontario from April 1st, 2009, to December 31st, 2017. Predictor variables will be fully prespecified based on a literature review of etiological studies and existing prognostic tools, and on subject-matter expertise; they will be categorized as follows: sociodemographic factors, comorbidities, previous interventions, functional status, nutritional status, admission information, previous health care utilization. Data-driven selection of predictors will be avoided. Continuous predictors will be modelled as restricted cubic splines. The outcome variable will be mortality within 1 year of admission, which will be modelled as a binary variable, such that a logistic regression model will be estimated. Predictor and outcome variables will be derived from linked population-level healthcare administrative databases. The validation cohort will comprise about 63,000 dementia patients, who were admitted to hospital in Ontario from January 1st, 2018, to March 31st, 2019. Model performance, measured by predictive accuracy, discrimination, and calibration, will be assessed using internal (temporal) validation. Calibration will be evaluated in the total validation cohort and in subgroups of importance to clinicians and policymakers. The final model will be based on the full cohort. We seek to develop and validate a clinical prediction tool to estimate the risk of 1-year mortality among hospitalized patients with dementia. The model would be integrated into the electronic medical records of hospitals to automatically output 1-year mortality risk upon hospitalization. The tool could serve as a trigger for advance care planning and inform access to specialist palliative care services with prognosis-based eligibility criteria. Before implementation, the tool will require external validation and study of its potential impact on clinical decision-making and patient outcomes. NCT05371782.

Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers

BackgroundThis study aimed to develop and validate an interpretable machine-learning model that utilizes clinical features and inflammatory biomarkers to predict the risk of in-hospital mortality in critically ill patients suffering from sepsis.MethodsWe enrolled all patients diagnosed with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.2.0), eICU Collaborative Research Care (eICU-CRD 2.0), and the Amsterdam University Medical Centers databases (AmsterdamUMCdb 1.0.2). LASSO regression was employed for feature selection. Seven machine-learning methods were applied to develop prognostic models. The optimal model was chosen based on its accuracy, F1 score and area under curve (AUC) in the validation cohort. Moreover, we utilized the SHapley Additive exPlanations (SHAP) method to elucidate the effects of the features attributed to the model and analyze how individual features affect the model’s output. Finally, Spearman correlation analysis examined the associations among continuous predictor variables. Restricted cubic splines (RCS) explored potential non-linear relationships between continuous risk factors and in-hospital mortality.Results3535 patients with sepsis were eligible for participation in this study. The median age of the participants was 66 years (IQR, 55–77 years), and 56% were male. After selection, 12 of the 45 clinical parameters collected on the first day after ICU admission remained associated with prognosis and were used to develop machine-learning models. Among seven constructed models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with an AUC of 0.94 and an F1 score of 0.937 in the validation cohort. Feature importance analysis revealed that Age, AST, invasive ventilation treatment, and serum urea nitrogen (BUN) were the top four features of the XGBoost model with the most significant impact. Inflammatory biomarkers may have prognostic value. Furthermore, SHAP force analysis illustrated how the constructed model visualized the prediction of the model.ConclusionsThis study demonstrated the potential of machine-learning approaches for early prediction of outcomes in patients with sepsis. The SHAP method could improve the interoperability of machine-learning models and help clinicians better understand the reasoning behind the outcome.

The association between obesity severity and food reward in adolescents with obesity: a one-stage individual participant data meta-analysis.

Food reward and cue reactivity have been linked prospectively to problematic eating behaviours and excess weight gain in adults and children. However, evidence to date in support of an association between degree of adiposity and food reward is tenuous. A non-linear relationship between reward sensitivity and obesity degree has been previously proposed, suggesting a peak is reached in mild obesity and decreases in more severe obesity in a quadratic fashion. To investigate and characterise in detail the relationship between obesity severity, body composition, and explicit and implicit food reward in adolescents with obesity. Data from seven clinical trials in adolescents with obesity were aggregated and analysed in an independent participant data meta-analysis. Linear and curvilinear relationships between the degree of obesity and explicit and implicit reward for sweet and high fat foods were tested in fasted and fed states with BMI-zscore as a continuous and discrete predictor using clinically recognised partitions. Although positive associations between obesity severity and preference for high-fat (i.e. energy dense) foods were observed when fasted, none reached significance in either analysis. Conversely, adiposity was reliably associated with lower reward for sweet, particularly when measured as implicit wanting (p = 0.012, ηp2 = 0.06), independent of metabolic state. However, this significant association was only observed in the linear model. Fat distribution was consistently associated with explicit and implicit preference for high-fat foods. A limited relationship was demonstrated between obesity severity and food reward in adolescents, although a lower preference for sweet could be a signal of severe obesity in a linear trend. Obesity is likely a heterogenous condition associated with multiple potential phenotypes, which metrics of body composition may help define. NCT02925572: https://classic. gov/ct2/show/NCT02925572 . NCT03807609: https://classic. gov/ct2/show/NCT03807609 . NCT03742622: https://classic. gov/ct2/show/NCT03742622 . NCT03967782: https://classic. gov/ct2/show/NCT03967782 . NCT03968458: https://classic. gov/ct2/show/NCT03968458 . NCT04739189: https://classic. gov/ct2/show/NCT04739189 . NCT05365685: https://www. gov/study/NCT05365685?tab=history .

Arthrocentesis of suspected septic sternoclavicular arthritis: microbial yield and predictors of culture positivity.

Evaluate the microbial yield and factors predicting culture positivity for image-guided arthrocentesis of suspected septic sternoclavicular (SC) arthritis. An electronic health record search identified image-guided SC joint aspirations for suspected septic arthritis. Data was extracted by retrospective chart review including patient demographics, procedure characteristics, pre-procedure lab testing, joint culture results, final SC joint diagnoses and any effect of positive synovial cultures on subsequent antibiotic therapy. Factors associated with positive joint fluid cultures were assessed using a Chi-squared test for categorical predictors and logistic regression for continuous predictors. A total of 31 SC arthrocenteses met inclusion criteria with most (81%) performed using ultrasound guidance. Synovial fluid was successfully aspirated in 19/31 (61%) of cases, and in all other cases lavage fluid was successfully obtained. Synovial cultures were positive in 9/31 (29%) of cases. A final diagnosis of septic arthritis was assigned to 20/31 cases (65%) in which 9/20 (45%) had positive synovial cultures. There was no statistically significant association between synovial culture positivity and risk factors for septic arthritis, positive blood cultures, pre-aspiration antibiotics and whether synovial fluid or lavage fluid was cultured. Serum white blood cell count (WBC) and erythrocyte sedimentation rate (ESR) demonstrated statistically significant positive correlation with positive synovial cultures. Arthrocentesis is effective for microbial speciation in SC septic arthritis, and diagnostic yield may be increased with lavage when encountering a dry tap. Normal serum WBC and ESR values indicate an extremely low likelihood of positive synovial cultures.

A Retrospective Study on tDCS Treatment in Patients with Drug-Resistant Chronic Pain.

Background. Transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) has an analgesic effect superior to a placebo in chronic pain. Some years ago, tDCS was implemented at the Hospital Nacional of Paraplegics (Toledo, Spain) to treat patients with pharmacological resistance to chronic pain. Objective. The main objectives of this study with tDCS were (1) to confirm the safety of one-year treatment; (2) to estimate the number of patients after one year in treatment; (3) to describe the effects of tDCS on the pain intensity during one-year treatment; and (4) to identify factors related to treatment success. Methods. This was a retrospective study conducted at the National Hospital for Paraplegics with 155 patients with pharmacologically resistant chronic pain. Anodal tDCS was applied over the M1 for 20 min at 1.5 mA for 10 treatment sessions from Monday to Friday (Induction phase), followed by 2-3 sessions per month (Maintenance phase). Pain intensity was assessed using a Visual Analogue Scale (VAS). Results. Anodal tDCS on M1 confirmed the reduction in the pain intensity. Moreover, 58% of outpatients completed one year of treatment. Only the VAS values obtained during the baseline influenced the response to treatment. Patients with a very high VAS at the baseline were more likely to not respond adequately to tDCS treatment. Conclusions. Anodal tDCS over M1 is an adequate therapy (safe and efficient) to treat drug-resistant chronic pain. Moreover, pain intensity at the start of treatment could be a predictor of patients' continuity with tDCS for at least one year.

A cell-based association data integration method for locating potential drilling targets: A case study at the Dalli Cu–Au porphyry deposit in Iran

The presence of various spatial exploration proxies (predictor patterns) as vectors to distal porphyry mineralization in surrounding areas of a known outcropping mineralized porphyry center at the Dalli Cu–Au deposit in Iran provided evidence for the occurrence of similar porphyry mineralization in the subsurface. Factor analysis was applied to soil geochemical data from the Dalli South Hill area to define favorable geochemical variables and to create surface multi-element anomaly maps reflecting the presence of porphyry Cu–Au mineralization in the subsurface. Reduction-to-pole and analytical signal filters were applied to ground total magnetic field data to define high magnetic signatures that could be associated with potassic alteration and porphyry mineralization in the subsurface. Then, a data-driven cell based association (CBA) data integration method was applied to the favorable spatial proxies to define their association with Cu–Au mineralization and locate subtle high potential targets (cells) for drilling. The conventional CBA algorithms did not consider weights for the spatial proxies, and by using the ascendant hierarchical clustering (AHC) algorithm the results were classified into a binary map to show favorable and unfavorable cells with scores of 1 and 0, respectively. Predicting high potential targets for high-priced drilling, just based on using the binary map, could be very risky. Therefore, an innovative CBA data integration approach was employed to assign proper weights between 0 and 1 to the spatial proxies by calculating the importance of the presence of an exploration proxy in a cell using training (labeled) trenching geochemical data. A final continuous predictor map was generated and it successfully mapped the known outcropping porphyry center and its subsurface extension to the northeast as a high prospectivity class. This map also identified a narrow NE-trending high potential target (a few cells) of 200 m long in the southern part of the known porphyry center for drilling.

Treatment Continuity and Bone Marrow Suppression in Whole-Brain and Whole-Spinal Cord Radiotherapy for Medulloblastoma Patients.

This study investigated the factors influencing treatment continuity and bone marrow suppression in whole-brain and whole-spinal cord radiotherapy for medulloblastoma, providing a clinical reference for mitigating the impact of hematological suppression on radiotherapy continuity. A retrospective analysis was conducted on patients with medulloblastoma who underwent craniospinal irradiation (CSI) radiotherapy at our hospital between August 2019 and December 2023. According to the inclusion and exclusion criteria, a total of 87 patients were enrolled. The bone marrow suppression status, clinical data, and radiotherapy dose data of the enrolled patients were recorded, and correlation analyses were performed. Based on the correlation results, further group comparisons were subsequently conducted. Overall, 22.99% (20 out of 87) of the patients experienced treatment interruption (median duration, 6.5 [5, 8] days), typically during the 12th (7.5, 14.75) radiotherapy session. Treatment continuity was weakly correlated with age and treatment modality, and the timing of interruptions was weakly correlated with dosage and treatment modality. Bone marrow suppression severity was weakly correlated with age, body mass index (BMI), and treatment modality. Treatment modality and age were found to be independent predictors of treatment continuity and the degree of bone marrow suppression, respectively. Subgroup comparisons revealed differences in the severity of bone marrow suppression, grade of hematological toxicity, and timing of interruption depending on the treatment modality, dosage, and sex (P < .05). Timely monitoring of hematological changes, especially in the middle and posterior segments after radiotherapy, is crucial. Treatment with helical tomotherapy, male sex, younger age, and lower BMI during radiotherapy are indicators of greater clinical attention.

The determinants of COVID-19 case reporting across Africa.

According to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the case reporting and classify the African countries using the case reporting rates and the significant determinants. We used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 34 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries. 21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central. Overall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection.

Regresi Logistik Ordinal untuk Memodelkan Predikat Lulusan Perguruan Tingggi

Logistic regression is an alternative model that can model the relationship between a categorical response variable and one or more categorical, continuous predictor variables, or a combination of categorical and continuous predictor variables. Based on the number of categories in the response variable, the logistic regression model consists of a dichotomous logistic regression model and a polychotomous regression model. The dichotomous logistic regression model is a logistic regression model that has two categories in the response variable and has a Bernoulli distribution. In comparison, the polychotomous logistic regression model is a logistic regression model that has three or more categories and a multinomial distribution. The polychotomous logistic regression model is divided into two models, namely the multinomial logistic regression model and ordinal logistic regression. This research aims to examine ordinal logistic regression modeling and its application to the predicate of graduates of the undergraduate program at the Faculty of Mathematics and Natural Sciences, Mulawarman University (FMIPA UNMUL) for the 2020 graduation period. The results of the research show that the factors that have a significant influence on the predicate of graduates of the FMIPA UNMUL undergraduate program are gender and admission route. Female graduates of the FMIPA UNMUL undergraduate program have a greater chance of achieving satisfactory and very satisfactory predicates compared to achieving a cum laude predicate. Graduates of the FMIPA UNMUL undergraduate program who are accepted through the SMMPTN admission route have a lower chance of achieving satisfactory and very satisfactory predicates compared to achieving a cum laude predicate.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study.

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models. We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed. A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods. The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

CPAP resumption after a first termination and impact on all-cause mortality in France.

Continuation of continuous positive airway pressure (CPAP) therapy after initial prescription has been shown to reduce all-cause mortality versus therapy termination. However, there is a lack of data on the rates and impact of resuming CPAP in patients with obstructive sleep apnoea (OSA). This analysis determined the prevalence of CPAP resumption in the year after termination, characterised determinants of CPAP resumption, and examined the impact of CPAP resumption on all-cause mortality. French national health insurance reimbursement system data for adults aged ≥18 years were used. CPAP prescription was identified by specific treatment codes. Patients who resumed CPAP after first therapy termination and continued to use CPAP for 1 year were matched with those who resumed CPAP then terminated therapy for a second time. Out of 103 091 individuals with a first CPAP termination, 26% resumed CPAP over the next 12 months, and 65% of these were still using CPAP 1 year later. Significant predictors of CPAP continuation after resumption included male sex, hypertension and CPAP prescription by a pulmonologist. In the matched population, the risk of all-cause death was 38% lower in individuals who continued using CPAP after therapy resumption versus those who had a second therapy discontinuation (hazard ratio 0.62, 95% CI 0.48-0.79; p=0.0001). These data suggest that individuals with OSA who fail initial therapy with CPAP should be offered a second trial with the device to ensure that effective therapy is not withheld from those who might benefit.