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PROBLEM: Midwifery Continuity of Care (MCoC) remains inaccessible for most Australian women; this is especially true in rural and regional areas. BACKGROUND: Strong evidence demonstrates MCoC models improve experiences for women and their babies and are also shown to improve midwifery workforce wellbeing. However, implementation and upscale remains limited. AIM: To explore the views and experiences of implementing MCoC for both staff and women, understanding their experiences, concerns and solutions in a regional context. METHODS: Qualitative data was collected via focus groups with women and healthcare staff, at six and twelve month post implementation. Data was thematically analysed using Braun and Clarke six step process. FINDINGS: The findings support that 'women love it' and midwives working in the new MCoC model 'loved their job'. The major concern was that not all women could access the model and disconnected communication was problematic during implementation. 'Sharing stories' was a solution to overcoming these issues and promoting the positive impact of MCoC - in particular ways of working and adaption to an all-risk midwifery group practice. DISCUSSION: This study supports widespread evidence that MCoC is valued by both women and staff. In a regional context it is important to recognise challenges faced during implementation and identifying solutions that other maternity services could consider when implementing MCoC. CONCLUSION: The study offers strong recommendation for regional areas to consider MGP to maintain safe, quality local maternity services.

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Investigating the optimal laser processing parameters for industrial purposes can be time-consuming. Moreover, an exact analytic model for this purpose has not yet been developed due to the complex mechanisms of laser processing. The main goal of this study was the development of a backpropagation neural network (BPNN) with a grey wolf optimization (GWO) algorithm for the quick and accurate prediction of multi-input laser etching parameters (energy, scanning velocity, and number of exposures) and multioutput surface characteristics (depth and width), as well as to assist engineers by reducing the time and energy require for the optimization process. The Keras application programming interface (API) Python library was used to develop a GWO-BPNN model for predictions of laser etching parameters. The experimental data were obtained by adopting a 30 W laser source. The GWO-BPNN model was trained and validated on experimental data including the laser processing parameters and the etching characterization results. The R2 score, mean absolute error (MAE), and mean squared error (MSE) were examined to evaluate the prediction precision of the model. The results showed that the GWO-BPNN model exhibited excellent accuracy in predicting all properties, with an R2 value higher than 0.90.

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BACKGROUND: An inherent difference exists between male and female bodies, the historical under-representation of females in clinical trials widened this gap in existing healthcare data. The fairness of clinical decision-support tools is at risk when developed based on biased data. This paper aims to quantitatively assess the gender bias in risk prediction models. We aim to generalize our findings by performing this investigation on multiple use cases at different hospitals. METHODS: First, we conduct a thorough analysis of the source data to find gender-based disparities. Secondly, we assess the model performance on different gender groups at different hospitals and on different use cases. Performance evaluation is quantified using the area under the receiver-operating characteristic curve (AUROC). Lastly, we investigate the clinical implications of these biases by analyzing the underdiagnosis and overdiagnosis rate, and the decision curve analysis (DCA). We also investigate the influence of model calibration on mitigating gender-related disparities in decision-making processes. RESULTS: Our data analysis reveals notable variations in incidence rates, AUROC, and over-diagnosis rates across different genders, hospitals and clinical use cases. However, it is also observed the underdiagnosis rate is consistently higher in the female population. In general, the female population exhibits lower incidence rates and the models perform worse when applied to this group. Furthermore, the decision curve analysis demonstrates there is no statistically significant difference between the model's clinical utility across gender groups within the interested range of thresholds. CONCLUSION: The presence of gender bias within risk prediction models varies across different clinical use cases and healthcare institutions. Although inherent difference is observed between male and female populations at the data source level, this variance does not affect the parity of clinical utility. In conclusion, the evaluations conducted in this study highlight the significance of continuous monitoring of gender-based disparities in various perspectives for clinical risk prediction models.

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Apparent thermal conductivity of soil (λ) as a function of soil water content (θ), i.e., λ(θ) is needed to determine the heat flow in soil. The function of λ(θ) can be used in heat and water flow models for simplicity. The objective of this study was to develop a sigmoidal model based on logistic equation for entire range of soil water contents and a wide range of soil textures that can be used in simulation of heat and water flow in respected modes. Further, performance of the developed sigmoidal model along with two other models in literature was evaluated. In the proposed sigmoidal model, the constants of this model are estimated based on empirical multivariate equations by using soil sand content and bulk density. The sigmoidal model was validated with good accuracy for a wide range of soil textures, as the relationship between the measured and predicted λ showed slope and intercept values of nearly 1.0 and 0.0, respectively. Comparison of the results obtained by sigmoidal model with those obtained from Johansen and Lu et al. models indicated that, the sigmoidal model was superior to the other two models in prediction of λ for a wide range of soil textures and soil water contents. Furthermore, comparison with a recently proposed model by Xiong et al. indicated that our sigmoidal model is superior. Therefore, our developed sigmoidal model can be used in heat and water flow models to predict the soil temperature and heat flow.

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Accurate estimation of climate change impacts on catchment hydrology is essential for effective future water management. The efficacy of such estimations is dependent on proper climate model selection. In this study, an attempt was made to formulate a methodology for climate model selection, evaluating eight climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models were assessed for their ability to simulate variables used in hydrological studies and large-scale atmospheric circulation influencing rainfall in Australia. Five statistical indicators Root Mean Square Error (RMSE), Spatial Correlation (SC), Percentage Bias (Pbias), Normalized Root Mean Square Error (NRMSE), and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the performance, and the models were ranked through Compromise Programming (CP), a multiple criteria decision making technique. Results show that HadGEM3-GC31-LL performed well in most of the categories considered and was top top-ranked model overall followed by GFDL-ESM4, CESM2-CAM6-RT, and CanESM5 for Australia. Conversely, MIROC6 consistently ranked lower in most of the categories. In the context of simulating hydrological variables, CESM2-CAM6-RT, HadGEM3-GC31-LL, and GFDL-ESM4 emerged as the top three models. The robustness of the proposed methodology suggests its applicability for model selection, making it a replicable approach for climate change impact assessment studies in diverse regions.

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In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.

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Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called "sniffer technique") and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 ("best model"), where all significant traits were included; model 2 ("on-farm model"), where DMI was excluded; and model 3 ("reduced on-farm model"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

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As the economic level of individuals rises, so too does the demand for mutton. Enhancing the breeds of mutton sheep not only boosts production efficiency and economic benefits but also fosters the sustainable growth of the mutton sheep breeding industry. Thus, this study examines the early growth and reproductive traits of Tianmu Sainuo sheep, analyzing the genetic interactions among these traits to furnish a theoretical foundation for refining breeding strategies and expediting the genetic advancement of this breed. The investigation compiled 29,966 data entries, involving 111 sires for birth weight (BWT) and 113 for other metrics. The data encompassed 10,415 BWT records from 1,633 dams, 12,753 weaning weight (WWT) records from 1,570 dams, 12,793 average daily gain (ADG) records from 1,597 dams, and 13,594 litter size (LS) records from 1,499 dams. Utilizing the GLM procedure in SAS 9.2 software, the study analyzed the non-genetic influences on lamb BWT, WWT, ADG, and LS. Concurrently, DMU software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving genetic parameters. The findings reveal that birth year (BY), birth quarter (BQ), birth type (BT), age of mother (AM), and birth sex (BS) exerted significant impacts on BWT, WWT, and ADG (p < 0.01). Additionally, BQ and AM significantly influenced LS (p < 0.01). The most accurate genetic evaluation model determined the heritability of BWT, WWT, ADG, and LS to be 0.0695, 0.0849, 0.0777, and 0.1252, respectively.

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Due to the rapid economic development of globalization and the intensification of economic and trade exchanges, cross-international and regional carbon emissions have become increasingly severe. Governments worldwide establish laws and regulations to protect their countries' environmental impact. Therefore, selecting robustness evaluation models and metrics is an urgent research topic. This article proves the reliability and scientific of the assessment data through literature coupling evaluation, multidisciplinary coupling mathematical model and international engineering case analysis. The innovation of this project's research lies in the comprehensive analysis of the complex coupling effects of various discrete data and uncertainty indicators on the research model across international projects and how to model and evaluate interactive effects accurately. This article provides scientific measurement standards and data support for governments worldwide to formulate carbon tariffs and carbon emission policies. Case analysis data shows that the carbon emission ratio of exporting and importing countries is 0.577:100; the carbon trading quota ratio is 32.50:100.

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PURPOSE: The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients. METHODS: This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. RESULTS: This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855-0.927) and a Brier score of 0.012. Furthermore, the Hosmer-Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856-0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5. CONCLUSION: A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification.

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Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

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In many modern machine learning applications, changes in covariate distributions and difficulty in acquiring outcome information have posed challenges to robust model training and evaluation. Numerous transfer learning methods have been developed to robustly adapt the model itself to some unlabeled target populations using existing labeled data in a source population. However, there is a paucity of literature on transferring performance metrics, especially receiver operating characteristic (ROC) parameters, of a trained model. In this paper, we aim to evaluate the performance of a trained binary classifier on unlabeled target population based on ROC analysis. We proposed Semisupervised Transfer lEarning of Accuracy Measures (STEAM), an efficient three-step estimation procedure that employs (1) double-index modeling to construct calibrated density ratio weights and (2) robust imputation to leverage the large amount of unlabeled data to improve estimation efficiency. We establish the consistency and asymptotic normality of the proposed estimator under the correct specification of either the density ratio model or the outcome model. We also correct for potential overfitting bias in the estimators in finite samples with cross-validation. We compare our proposed estimators to existing methods and show reductions in bias and gains in efficiency through simulations. We illustrate the practical utility of the proposed method on evaluating prediction performance of a phenotyping model for rheumatoid arthritis (RA) on a temporally evolving EHR cohort.

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Modelling approaches to estimate the bioaccumulation of organic chemicals by earthworms are important for improving the realism in risk assessment of chemicals. However, the applicability of existing models is uncertain, partly due to the lack of independent datasets to test them. This study therefore conducted a comprehensive literature review on existing empirical and kinetic models that estimate the bioaccumulation of organic chemicals in earthworms and gathered two independent datasets from published literature to evaluate the predictive performance of these models. The Belfroid et al. (1995a) model is the best-performing empirical model, with 91.2% of earthworm body residue simulations within an order of magnitude of observation. However, this model is limited to the more hydrophobic pesticides and to the earthworm species Eisenia fetida or Eisenia andrei. The kinetic model proposed by Jager et al. (2003b) which out-performs that of Armitage and Gobas (2007), predicted uptake of PCB 153 in the earthworm E. andrei to within a factor of 10. However, the applicability of Jager et al.'s model to other organic compounds and other earthworm species is unknown due to the limited evaluation dataset. The model needs to be parameterised for different chemical, soil, and species types prior to use, which restricts its applicability to risk assessment on a broad scale. Both the empirical and kinetic models leave room for improvement in their ability to reliably predict bioaccumulation in earthworms. Whether they are fit for purpose in environmental risk assessment needs careful consideration on a case by case basis.

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BACKGROUND AND OBJECTIVES: Despite being clinically utilized for the treatment of infections, the limited therapeutic range of polymyxin B (PMB), along with considerable interpatient variability in its pharmacokinetics and frequent occurrence of acute kidney injury, has significantly hindered its widespread utilization. Recent research on the population pharmacokinetics of PMB has provided valuable insights. This study aims to review relevant literature to establish a theoretical foundation for individualized clinical management. METHODS: Follow PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, Pop-PK studies of PMB were searched in PubMed and EMBASE database systems from the inception of the database until March 2023. RESULT: To date, a total of 22 population-based studies have been conducted, encompassing 756 subjects across six different countries. The recruited population in these studies consisted of critically infected individuals with multidrug-resistant bacteria, patients with varying renal functions, those with cystic fibrosis, kidney or lung transplant recipients, patients undergoing extracorporeal membrane oxygenation (ECMO) or continuous renal replacement therapy (CRRT), as well as individuals with obesity or pediatric populations. Among these studies, seven employed a one-compartmental model, with the range of typical clearance (CL) and volume (Vc) being 1.18-2.5L /h and 12.09-47.2 L, respectively. Fifteen studies employed a two-compartmental model, with the ranges of the clearance (CL) and volume of the central compartment (Vc), the volume of the peripheral compartment (Vp), and the intercompartment clearance (Q) were 1.27-8.65 L/h, 5.47-38.6 L, 4.52-174.69 L, and 1.34-24.3 L/h, respectively. Primary covariates identified in these studies included creatinine clearance and body weight, while other covariates considered were CRRT, albumin, age, and SOFA scores. Internal evaluation was conducted in 19 studies, with only one study being externally validated using an independent external dataset. CONCLUSION: We conclude that small sample sizes, lack of multicentre collaboration, and patient homogeneity are the primary reasons for the discrepancies in the results of the current studies. In addition, most of the studies limited in the internal evaluation, which confined the implementation of model-informed precision dosing strategies.

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Replication provides a confrontation of psychological theory, not only in experimental research, but also in model-based research. Goodness of fit (GOF) of the original model to the replication data is routinely provided as meaningful evidence of replication. We demonstrate, however, that GOF obscures important differences between the original and replication studies. As an alternative, we present Bayesian prior predictive similarity checking: a tool for rigorously evaluating the degree to which the data patterns and parameter estimates of a model replication study resemble those of the original study. We apply this method to original and replication data from the National Comorbidity Survey. Both data sets yielded excellent GOF, but the similarity checks often failed to support close or approximate empirical replication, especially when examining covariance patterns and indicator thresholds. We conclude with recommendations for applied research, including registered reports of model-based research, and provide extensive annotated R code to facilitate future applications of prior predictive similarity checking.

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Background: ORIN1001, a first-in-class oral IRE1-α endoribonuclease inhibitor to block the activation of XBP1, is currently in clinical development for inhibiting tumor growth and enhancing the effect of chemical or targeted therapy. Early establishment of a population pharmacokinetic (PopPK) model could characterize the pharmacokinetics (PK) of ORIN1001 and evaluate the effects of individual-specific factors on PK, which will facilitate the future development of this investigational drug. Methods: Non-linear mixed effect model was constructed by Phoenix NLME software, utilizing the information from Chinese patients with advanced solid tumors in a phase I clinical trial (Register No. NCT05154201). Statistically significant PK covariates were screened out by a stepwise process. The final model, after validating by the goodness-of-fit plots, non-parametric bootstrap, visual predictive check and test of normalized prediction distribution errors, was further applied to simulate and evaluate the impact of covariates on ORIN1001 exposure at steady state up to 900 mg per day as a single agent. Results: A two-compartment model with first-order absorption (with lag-time)/elimination was selected as the best structural model. Total bilirubin (TBIL) and lean body weight (LBW) were considered as the statistically significant covariates on clearance (CL/F) of ORIN1001. They were also confirmed to exert clinically significant effects on ORIN1001 steady-state exposure after model simulation. The necessity of dose adjustments based on these two covariates remains to be validated in a larger population. Conclusion: The first PopPK model of ORIN1001 was successfully constructed, which may provide some important references for future research.

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The uptake and elimination kinetics of pesticides from soil to earthworms are important in characterising the risk of pesticides to soil organisms and the risk from secondary poisoning. However, the understanding of the relative importance of chemical, soil, and species differences in determining pesticide bioconcentration into earthworms is limited. Furthermore, there is insufficient independent data in the literature to fully evaluate existing predictive bioconcentration models. We conducted kinetic uptake and elimination experiments for three contrasting earthworm species (Lumbricus terrestris, Aporrectodea caliginosa, Eisenia fetida) in five soils using a mixture of five pesticides (log Kow 1.69 - 6.63). Bioconcentration increased with pesticide hydrophobicity and decreased with soil organic matter. Bioconcentration factors were comparable between earthworm species for hydrophilic pesticides due to the similar water content of earthworm species. Inter-species variations in bioconcentration of hydrophobic pesticides were primarily accounted for by earthworm lipid content and specific surface area (SSA). Existing bioconcentration models either failed to perform well across earthworm species and for more hydrophilic compounds (log Kow < 2) or were not parameterised for a wide range of compounds and earthworm species. Refined models should incorporate earthworm properties (lipid content and SSA) to account for inter-species differences in pesticide uptake from soil.

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An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds.

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INTRODUCTION: Physiologically based pharmacokinetic (PBPK) modeling is a paradigm shift in this era for determining the exposure of drugs in pediatrics, geriatrics, and patients with chronic diseases where clinical trials are difficult to conduct. AREAS COVERED: This review has collated data regarding published PBPK models on chronic kidney disease (CKD), including the drug and system-specific input model parameters and model evaluation criteria. Four databases were used from 13th June 2023 to 10th July 2023 for identifying the relevant studies that met the inclusion/exclusion criteria. Alterations in plasma protein (albumin/alpha-1 acid glycoprotein), gastric emptying time, hematocrit, small intestinal transit time, the abundance of cytochrome (CYP) 450 enzymes, glomerular filtration rate, and physicochemical parameters for different drugs were explicitly elaborated from earlier reported studies. Moreover, model evaluation depicted that models in CKD for most of the included drugs were within the allowed two-fold error range. EXPERT OPINION: This review will provide insights for researchers on applying PBPK models in managing patients with different levels of CKD to prevent undesirable side effects and increase the effectiveness of drug therapy.

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AIM: To develop a visual prediction model for gestational diabetes (GD) in pregnant women and to establish an effective and practical tool for clinical application. METHODS: To establish a prediction model, the modelling set included 1756 women enrolled in the Zunyi birth cohort, the internal validation set included 1234 enrolled women, and pregnant women in the Wuhan cohort were included in the external validation set. We established a demographic-lifestyle factor model (DLFM) and a demographic-lifestyle-environmental pollution factor model (DLEFM) based on whether the women were exposed to environmental pollutants. The least absolute shrinkage and selection lasso-logistic regression analyses were used to identify the independent predictors of GD and construct a nomogram for predicting its occurrence. RESULTS: The DLEFM regression analysis showed that a family history of diabetes (odd ratio [OR] 2.28; 95% confidence interval [CI] 1.05-4.71), a history of GD in pregnant women (OR 4.22; 95% CI 1.89-9.41), being overweight or obese before pregnancy (OR 1.71; 95% CI 1.27-2.29), a history of hypertension (OR 2.61; 95% CI 1.41-4.72), sedentary time (h/day) (OR 1.16; 95% CI 1.08-1.24), monobenzyl phthalate (OR 1.95; 95% CI 1.45-2.67) and Q4 mono-ethyl phthalate concentration (OR 1.85; 95% CI 1.26-2.73) were independent predictors. The area under the receiver operating curves for the internal validation of the DLEFM and the DLFM constructed using these seven factors was 0.827 and 0.783, respectively. The calibration curve of the DLEFM was close to the diagonal line. The DLEFM was thus the more optimal model, and the one which we chose. CONCLUSIONS: A nomogram based on preconception factors was constructed to predict the occurrence of GD in the second and third trimesters. It provided an effective tool for the early prediction and timely management of GD.

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