Tag: statistics

Psychol Rep. 2025 Aug 15:332941251370231. doi: 10.1177/00332941251370231. Online ahead of print.

ABSTRACT

The primary objective of this study is to create a measurement tool that is both valid and reliable for assessing parental regret among mothers and fathers regarding their roles as parents. The study sample comprised 721 participants residing in Turkey, each of whom had at least one biological child and willingly took part in the study. The results of the exploratory factor analysis (EFA) indicated that there is a two-factor structure consisting of 21 elements: intrinsic factors (items reflecting internal psychological aspects of parenting regret) and extrinsic factors (items reflecting external circumstantial aspects of parenting regret). The confirmatory factor analysis (CFA) yielded model fit indices that supported the existence of a two-factor structure. The validity analyses met the necessary criteria for convergent-divergent and criterion-related validity. Cronbach’s alpha (α) coefficient and the equivalent halves method were used to examine the reliability of the measurement tool. The Cronbach’s alpha (α) coefficient was calculated to be .943, the correlation between the two halves was found to be .637, and the Spearman Brown coefficient was calculated to be .778. We determined the item-total test correlation and t-values obtained during item analysis to be statistically significant. Upon evaluating the validity and reliability analyses holistically, we found that the Parenting Regret Scale (PRS-TR) was capable of producing accurate and dependable measurements.

PMID:40817447 | DOI:10.1177/00332941251370231

Int J Lang Commun Disord. 2025 Sep-Oct;60(5):e70105. doi: 10.1111/1460-6984.70105.

ABSTRACT

PURPOSE: Clinical reasoning is essential for speech-language pathologists (SLPs) when addressing ill-defined questions in various clinical settings. This study focuses on the acquisition of clinical reasoning skills in SLP students, particularly their evolution with clinical experience. To achieve this, the study developed and validated the first cloud-based script concordance test (SCT) tailored for assessing clinical reasoning skills in SSD diagnosis.

METHODS: An expert panel of 15 SLPs (average experience of 20.3 years) helped develop and score the SCT, which was administered to 51 undergraduate students (22 sophomores, 18 juniors, and 11 seniors). Statistical analyses examined the predictive role and trend of clinical experience in three dimensions (utility, interpretation, and diagnosis).

RESULTS: A significant difference in mean SCT scores was found between the expert panel and student groups (p < 0.05). Trend analysis showed a significant impact of clinical experience on SCT performance across all dimensions (all Fs > 9.91, p < 0.001), with greater experience linked to better reasoning skills. Low-scoring items highlighted challenges with stimulability testing, indicating a lack of clinical consensus.

CONCLUSIONS: This study demonstrates that clinical reasoning skills in SSD assessment become more refined with accumulated experience. The SCT developed effectively differentiates reasoning abilities between experts and students, offering a valuable tool for advancing clinical decision-making in speech-language pathology. These findings have practical implications, empowering SLP educators to design effective training programs and preparing students for the challenges they may face in clinical practice.

WHAT THIS PAPER ADDS: What is already known on this subject? The knowledge base of clinical practitioners includes both content and its organization. Clinical reasoning is the cognitive ability to integrate, organize, and interpret information as a key aspect of expertise in evidence-based practice. Recently, script concordance tests, which use case-based scenarios to reflect clinical decision-making processes, have become a popular method for assessing these reasoning skills. What this paper adds to existing knowledge? This study is the first to empirically show that clinical reasoning skills in speech-language pathology, particularly in assessing speech sound disorders, are progressively refined through clinical experience. It highlights that students’ reasoning skills during internships appear to be influenced by supervisory clinical approaches, potentially affecting diagnostic abilities. These reasoning skills may improve with further clinical experience or targeted continuing education. Additionally, the study developed the first SCT focused on SSD evaluation, demonstrating its effectiveness in distinguishing between expert and student clinical reasoning and decision-making abilities. What are the potential or actual clinical implications of this work? A recent goal in clinical education is to make clinical reasoning processes more explicit and structured during training. Creating comprehensive case assessment scripts and designing concrete clinical thinking guidance programs integrated with effective measurement, such as the SCT developed in this study, could serve as teaching objectives for the clinical diagnosis of SSD.

PMID:40817427 | DOI:10.1111/1460-6984.70105

Med Phys. 2025 Aug;52(8):e18067. doi: 10.1002/mp.18067.

ABSTRACT

BACKGROUND: Accurate segmentation of high-risk clinical target volume (HRCTV) and organs-at-risk (OARs) is crucial for optimizing gynecologic brachytherapy treatment planning. However, performing this segmentation on Computed Tomography (CT) images remains particularly challenging due to anatomical variability, limited soft-tissue contrast, and the scarcity of annotated datasets. Compared to other radiotherapy domains, CT-based gynecologic brachytherapy segmentation is notably underrepresented in benchmarking studies.

PURPOSE: This study aims to improve the segmentation of HRCTV and OARs in gynecologic brachytherapy by introducing GynBTNet, a multi-stage learning framework. Through large-scale self-supervised pretraining and progressive finetuning, the model is designed to enhance anatomical representation learning and adapt effectively to domain-specific gynecologic structures, addressing the challenges of limited training data and complex anatomical variability.

METHODS: GynBTNet employs a three-stage training strategy: (1) self-supervised pretraining on large-scale CT datasets using sparse submanifold convolution to capture robust anatomical representations, (2) supervised finetuning on a multi-organ segmentation dataset to refine feature extraction, and (3) task-specific finetuning on the gynecologic brachytherapy dataset to optimize segmentation performance for clinical applications. In the third stage, 116 cases were used for training, while 29 cases were reserved for independent testing. The model was evaluated against state-of-the-art methods using the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95%), and Average Surface Distance (ASD). Overall statistical significance across models was assessed using the Friedman test. Post hoc pairwise comparisons were conducted using two-tailed paired permutation tests, with multi-comparison correction via the Benjamini-Hochberg procedure to control the false discovery rate. Cohen’s effect sizes were calculated to quantify the performance differences.

RESULTS: GynBTNet demonstrated consistent superiority over nnU-Net across all structures and achieved overall favorable performance compared to Swin-UNETR. The most substantial improvement was observed in HRCTV segmentation, where GynBTNet achieved a DSC of 0.837 ± 0.068, significantly higher than nnU-Net (p < 0.05) with a large effect size of +1.25, and superior to Swin-UNETR with a moderate-to-large effect size of +0.57. Boundary precision for HRCTV also improved significantly, with effect sizes in HD95% (-0.81 vs. nnU-Net, -0.52 vs. Swin-UNETR) and ASD (-1.20 vs. nnU-Net, -0.61 vs. Swin-UNETR). For bladder segmentation, GynBTNet reached a DSC of 0.940 ± 0.052, significantly outperforming nnU-Net (p < 0.05) with a large effect size of +1.28 and showing a small advantage over Swin-UNETR (effect size +0.26). In rectum segmentation, GynBTNet achieved a DSC of 0.842 ± 0.070, significantly exceeding nnU-Net (p < 0.05) with a large effect size of +1.17, and surpassing Swin-UNETR with an effect size of +0.54. For the uterus, GynBTNet significantly improved boundary accuracy compared to both nnU-Net and Swin-UNETR (p < 0.05), with effect sizes in ASD of -0.99 and -0.64. Segmentation of the sigmoid colon remained challenging, as GynBTNet provided only marginal DSC gains over nnU-Net with negligible effect sizes.

CONCLUSIONS: The proposed multi-stage learning strategy effectively enhances segmentation accuracy for gynecologic brachytherapy, leveraging large-scale self-supervised pretraining and progressive finetuning. By improving HRCTV and OARs delineation, GynBTNet has the potential to enhance treatment planning precision, minimize radiation exposure to critical structures, and improve patient outcomes.

PMID:40817425 | DOI:10.1002/mp.18067

Sci Rep. 2025 Aug 15;15(1):29946. doi: 10.1038/s41598-025-16015-3.

ABSTRACT

Focal cortical dysplasia (FCD) is the leading cause of drug-resistant epilepsy (DRE) in pediatric patients. Accurate detection of FCDs is crucial for successful surgical outcomes, yet remains challenging due to frequently subtle MRI findings, especially in children, whose brain morphology undergoes significant developmental changes. Automated detection algorithms have the potential to improve diagnostic precision, particularly in cases, where standard visual assessment fails. This study aimed to evaluate the performance of automated algorithms in detecting FCD type II in pediatric patients and to examine the impact of adult versus pediatric templates on detection accuracy. MRI data from 23 surgical pediatric patients with histologically confirmed FCD type II were retrospectively analyzed. Three imaging-based detection algorithms were applied to T1-weighted images, each targeting key structural features: cortical thickness, gray matter intensity (extension), and gray-white matter junction blurring. Their performance was assessed using adult and pediatric healthy controls templates, with validation against both predictive radiological ROIs (PRR) and post-resection cavities (PRC). The junction algorithm achieved the highest median dice score (0.028, IQR 0.038, p < 0.01 when compared with other algorithms) and detected relevant clusters even in MRI-negative cases. The adult template (median dice score 0.013, IQR 0.027) significantly outperformed the pediatric template (0.0032, IQR 0.023) (p < 0.001), highlighting the importance of template consistency. Despite superior performance of the adult template, its use in pediatric populations may introduce bias, as it does not account for age-specific morphological features such as cortical maturation and incomplete myelination. Automated algorithms, especially those targeting junction blurring, enhance FCD detection in pediatric populations. These algorithms may serve as valuable decision-support tools, particularly in settings where neuroradiological expertise is limited.

PMID:40817382 | DOI:10.1038/s41598-025-16015-3

Sci Rep. 2025 Aug 15;15(1):29959. doi: 10.1038/s41598-025-15783-2.

ABSTRACT

With the rapid development of the global financial market, the problem of financial fraud is becoming more and more serious, which brings huge economic losses to the market, consumers and investors and threatens the stability of the financial system. Traditional fraud detection methods based on rules and statistical analysis are difficult to deal with increasingly complex and evolving fraud methods, and there are problems such as poor adaptability and high false alarm rate. Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. The experiment is based on more than 1 million real financial transaction data. The results show that the Stacking model is significantly superior to the traditional single model in accuracy (95%), recall (93%) and F1 score (94%), and has stronger generalization ability and stability. Although the Stacking model has challenges in computing cost and delay, its advantages in fraud detection accuracy and robustness make it a powerful tool for financial institutions to improve their risk control ability. In the future, its real-time adaptability can be further optimized through online learning and incremental update mechanism.

PMID:40817346 | DOI:10.1038/s41598-025-15783-2

J Imaging Inform Med. 2025 Aug 15. doi: 10.1007/s10278-025-01644-x. Online ahead of print.

ABSTRACT

Coronary heart disease is one of the leading causes of global death. Early detection and accurate risk assessment are critical for improving patient health and reducing fatality rates. Recently, machine learning has emerged as a powerful approach for predicting heart disease by analyzing clinical data, which enables timely intervention and personalized treatment. Hence, this study aims to propose a reliable model for predicting coronary heart disease that integrates multi-source heart disease data with various machine learning models. This study employs heart disease datasets from four separate databases: Cleveland, Hungary, Switzerland, and VA Long Beach, provided by the UCI machine learning repository. Various machine learning models were utilized in this study, such as logistic regression, Naive Bayes, random forest, extreme gradient boost, K-nearest neighbors, decision trees, and support vector machines. These models were assessed using different evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and F1-score. The models with the highest accuracy were integrated into the proposed ensemble-learning model. The synthetic minority oversampling technique was implemented prior to training to address the issue of class imbalance, which is frequently observed in medical datasets. The proposed ensemble model achieved 98.46% accuracy, 96% precision, 100% recall, and a 98% F1-score. These findings demonstrate the effectiveness and robustness of the proposed model in accurately predicting coronary heart disease.

PMID:40817318 | DOI:10.1007/s10278-025-01644-x

Sci Rep. 2025 Aug 15;15(1):29973. doi: 10.1038/s41598-025-90641-9.

ABSTRACT

The seventh-biggest desert in the world, the Thar Desert, is home to a number of species that have adapted to the harsh desert environment. One of them seed purpose watermelon is an endemic, seed and vegetable purpose C3 cucurbitaceous xerophytes naturally grown from antiquity. Present investigation was undertaken to evaluate 138 seed purpose watermelon genotypes including three checks for phenology, fruits and seed yield related traits using multivariate approach. Analysis of variance revealed highly significant differences (p < 0.001) among the genotypes, blocks and its interaction for all the traits studied showed that a wide and significant variation existed among the genotypes and traits. Descriptive statistics revealed significant variation for days to fruit initiation (27-56 days), days to maturity (64-74 days), fruit diameter (66.07-129.50 mm), fruits per plant (1-5), fruit yield (40-248.25 q/ha), 100-seed weight (3.44-9.07 g) and seed yield (0.40-5.28 q/ha). The phenotypic coefficient of variation (PCV) was slightly higher than genotypic coefficient of variation (GCV) except for fruits per plant signifying little influence of environment on the expression of all the traits studied. High PCV and GCV were recorded for seed yield followed by fruit yield and fruits per plant indicating variation and scope of improvement through phenotypic selection. High heritability coupled with high genetic advance for all the traits except days to maturity revealed that they are predominantly governed by additive gene action and phenotypic selection will be effective. The highly significant positive associations (P < 0.01) of seed yield with fruit yield, fruits per plant, fruit diameter and 100-seed weight; 100-seed weight with fruit yield and days to maturity; fruit yield with fruits per plant and fruit diameter and fruits per plant with fruit diameter; implies that improving one or more component traits could result in genetic enhancement of seed yield in seed purpose watermelon. Cluster analysis for quantitative traits using unweighted pair group method of arithmetic averages (UPGMA) grouped the genotypes into eight clusters with varied number. The principal component analysis (PCA) revealed that most of the variation (99.91%) was accounted by first four PCA and fruit yield and fruit diameter have contributed most of the variation in dimensions 1 and 2. The extra early genotypes such as RMK2313, RMK2345 and RMK2353, RMK2324, multi-trait specific genotypes (fruits per plant, fruit yield and seed yield) viz., RMK23123, RMK23127, RMK23130 along with check GK-2, RMK2348 and RMK2365 for highest seed yield and RMK2355 exceptionally for both extra early and highest seed yield attributes could be used for selection or intercrossing in subsequent generations. This study identified the trait association among the different agro-morphological attributes and identified the trait specific genotypes in kalingada, which could help plant breeders select the best genotypes to improve fruit and seed yields.

PMID:40817286 | DOI:10.1038/s41598-025-90641-9

Sci Rep. 2025 Aug 15;15(1):29927. doi: 10.1038/s41598-025-15893-x.

ABSTRACT

The caregiver-patient dynamic is a complex relationship. While caregivers’ potential psychological and physical burdens have received much attention, few studies have focused on the patient’s perspective. This study investigated how the presence of a caregiver affects perceived stress, mental health and metabolic function in individuals with physical disabilities (PDis). The study included individuals with a PDis living with a full-time caregiver and those with a PDis living alone. The severity of perceived stress, the state of mental health, and the amount of social support were assessed using standardized questionnaires. Urine samples were collected and analyzed using a proton nuclear magnetic resonance spectroscopy-based metabolomics approach to investigate if different living situations affect biochemical pathways. Although there were no statistically significant differences in stress outcomes or mental health between the two groups, metabolomic analysis revealed a significant impact of the living situation on metabolic pathways, including histidine metabolism and alanine, aspartate and glutamate metabolism. Aspartic acid levels positively correlated with perceived stress and depressive symptoms in the Caregiver Group, while inosine positively correlated with stress in the Alone Group. This study highlights the unique psychological and metabolic profiles associated with a PDis based on living arrangements.

PMID:40817283 | DOI:10.1038/s41598-025-15893-x

BMC Plant Biol. 2025 Aug 16;25(1):1079. doi: 10.1186/s12870-025-07145-x.

ABSTRACT

BACKGROUND: Biological invasions pose significant ecological and socio-economic threats globally. Heracleum mantegazzianum (giant hogweed) is an invasive plant, extensively invading Europe and North America. It exerts negative impacts on ecosystems, native vegetation, and public health in the invaded range. Although H. mantegazzianum has not been reported from Turkey yet, ecological conditions of the country similar to those prevailing in its native and invaded ranges suggest a high introduction and spread risk for Turkey. Therefore, the current study predicted the introduction and future invasion risk of H. mantegazzianum in Turkey under current and future Coupled Model Intercomparison Project Phase 6 (CMIP6) projections.

METHODS: Maximum Entropy (MaxEnt) model was used to predict introduction and future invasion risk using occurrence data from native and invaded ranges and global environmental data. Only climatic data were used for modeling as future data for soil and socioeconomic attributes are currently unavailable. Multicollinearity among environmental variables was tested and 10 least correlated variables, i.e., bio1 (annual mean temperature), bio2 (mean diurnal range), bio4 (temperature seasonality), bio5 (max temperature of warmest month), bio6 (min temperature of coldest month), bio7 (temperature annual range), bio10 (mean temperature of warmest quarter), bio11 (mean temperature of coldest quarter), bio14 (precipitation of driest month), and bio15 (precipitation seasonality) were used to train and test the model. Furthermore, the model was optimized before training and testing. The model was trained and tested with 18,607 occurrence records of which 75% and 25% were split for training and testing, respectively. Future invasion risk was predicted under two CMIP6 climate change scenarios (SSP1-2.6 and SSP5-8.5). Predictive accuracy of the model was evaluated by area under the receiver operating characteristics curve (AUC), true skill statistics (TSS), sensitivity and specificity.

RESULTS: MaxEnt model predicted introduction and future invasion risk of H. mantegazzianum with high accuracy (AUC = 0.97 ± 0.02; TSS = 0.94 ± 0.04, Kappa = 0.92 ± 0.03, sensitivity = 93.40 ± 2.20, and specificity = 94.80 ± 3.40). The bio14, bio6 and bio1 had the highest permutation importance indicating that temperature and precipitation changes will mediate the introduction and future invasion of H. mantegazzianum. A total 4.2% of Turkey’s land area (31.2 thousand km²) was predicted highly suitable for the introduction of H. mantegazzianum in the Black Sea region under current climate. The CMIP6 climate projections suggest a ~ 50% decline in highly suitable habitats, and aggregation around the Black Sea coast.

CONCLUSION: Climate change is expected to reduce the overall range of H. mantegazzianum in Turkey but may intensify impacts in Black Sea region due to aggregation. Proactive monitoring and management strategies targeting high invasion risk areas guided by invasion risk maps from this study are urgently needed mitigate ecological and socio-economic consequences of H. mantegazzianum in Turkey.

PMID:40817242 | DOI:10.1186/s12870-025-07145-x

BMC Med Inform Decis Mak. 2025 Aug 15;25(1):310. doi: 10.1186/s12911-025-03091-8.

ABSTRACT

BACKGROUND: Cancer patients receiving targeted therapies need to prevent QTc prolongation and life-threatening cardiovascular (CV) events to maintain a balanced benefit-risk ratio. This study aimed to develop an optimal prediction model for QTc prolongation risk and estimate its risk probability in cancer patients treated with oral tyrosine kinase inhibitors (TKIs).

METHODS: This retrospective cohort study analyzed electronic medical records (EMR) of cancer patients newly treated with commonly used oral TKIs at a medical center between January 2016 and December 2020. QTc prolongation was defined as ≥ 450 ms in males and ≥ 470 ms in females using Bazett’s formula. The study followed four key steps: (1) Managing missing data, (2) Identifying important variables, (3) Training and testing the best prediction models, (4). Estimating risk probability and determining cut-off points. Both univariate logistic regression (LR) and supervised machine learning (ML) approaches were used for variable selection. The backward LR method and seven ML algorithms were applied to train and test the prediction models. The best model was identified based on model performance, fitting criteria, area under the receiver operating characteristic curve (AUROC), risk probability cut-off points, and clinical relevance.

RESULTS: The statistical 12-parameter model demonstrated excellent performance (AUROC = 0.89, sensitivity = 0.91, specificity = 0.75) and strong discrimination ability for risk probability prediction (AUROC = 0.78, cut-off = 0.46), outperforming other ML models. In the final best model: the baseline risk probability of QTc prolongation was 0.13, even in the absence of other contributing factors. Baseline QTc prolongation and a history of cardiovascular disease (excluding arrhythmia, cardiomyopathy, etc.) contributed the most to incremental risk probability (0.471 and 0.282, respectively), after controlling for other factors. The remaining 10 factors each contributed to an increased probability of QTc prolongation for more than 0.14 probability.

CONCLUSIONS: A logistic regression model utilizing 12 easily accessible variables from EMRs outperformed ML models in predicting the risk probability of QTc prolongation in cancer patients newly treated with five oral TKIs. These findings serve as a valuable clinical reference for integrating digital monitoring into cardiovascular care for cancer survivors undergoing targeted therapy with TKIs. They also underscore the importance of screening baseline ECG before initiating TKIs to assess the risk of QTc prolongation, facilitating early intervention and prevention in the future.

PMID:40817221 | DOI:10.1186/s12911-025-03091-8