Independent Test Research Articles

Racial disparities in end-of-life care among patients with neuroendocrine tumors.

60 Background: Due to the rising incidence of neuroendocrine tumors (NETs), the prevalence of NETs now surpasses many other malignancies. Due to a slower course of disease and lack of awareness among care teams, end-of-life (EOL) care is typically underutilized in patients with NETs. Owing to a heavy symptom burden (related to disease burden and/or functionality), patients with NETs often struggle due to lack of palliative care and eventually EOL care support. Prior studies have revealed that Black patients with advanced cancer are more likely to receive aggressive care and suffer a significant decline in quality of life at EOL. However, similar data investigating racial differences in EOL among patients with NETs are lacking. Methods: The National Inpatient Sample (NIS) was queried between years 2016-2020 to identify all hospitalizations (regardless of site of origin) with NETs utilizing the appropriate ICD-10 codes (neuroendocrine carcinomas were excluded). Hospitalizations of White and Black patients with documented inpatient mortality events were then extracted. Demographic and clinical data were analyzed using independent sample t-test, Chi-square test, and binary logistic regression (adjusted for age, gender, and Charlson comorbidity index or CCI). NIS discharge weights were applied to calculate national estimates. Statistical analyses were performed with SPSS 28.0. A p-value of <0.05 was considered statistically significant. Results: A total of 6,265 hospitalizations with NETs were included, of which 5,125 (81.8%) and 1,140 (18.2%) had White and Black patients respectively. Black patients were younger, more likely to be females, and less likely to have a do not resuscitate (DNR) code status. There was no significant difference in CCI between the groups. Palliative care consultation was less likely to be performed among Black patients (adjusted OR=0.70, p<.001). Black patients also had longer mean length of stay in the hospital (10.25 vs 8.42 days, p<.001). Black patients were also more likely to receive aggressive care in the form of mechanical ventilation at EOL (Table). Conclusions: This is the largest analysis demonstrating significant racial disparities in EOL care among patients with NETs. Black patients had longer inpatient stay, were less likely to receive inpatient palliative care consultation, have a DNR order and more likely to receive aggressive care at EOL. These findings may have implications for informing healthcare decision making for EOL care among Black patients with NETs. WhiteN=5125 BlackN=1140 aOR (95% CI) p-value Age (years) 67.67 ± 11.89 65.14 ± 11.29 - <.001 Females 46.1% 50.9% - .004 CCI 11.0 ± 3.22 10.9 ± 3.42 - .538 Mean length of stay 8.42 ± 10.03 10.25 ± 15.75 - <.001 Palliative 62.6% 54.5% 0.70 (.61-.80) <.001 DNR 64.9% 60.1% 0.80 (.70-.92) .002 Blood transfusion 13.2% 15.4% 1.18 (.98-1.41) .052 Mechanical ventilation 28.8% 38.6% 1.55 (1.35-1.77) <.001 Vasopressor 9% 9.6% 1.07 (.86-1.33) .47

Pengaruh Model Pembelajaran Kooperatif Tipe Inside Outside Circle Berbantuan Media Audio Visual terhadap Kemampuan Berpikir Kritis Siswa SMP dalam Pembelajaran Matematika

One of the factors that causes low critical thinking skills of students is the use of inappropriate learning models in the learning process. The learning process has an important role in developing students' critical thinking skills. One of the learning models that is considered effective in improving students' critical thinking skills is the Inside Outside Circle model. So this study aims to determine the Effect of the Inside Outside Circle Cooperative Learning Model Assisted by Audio Visual Media on the Critical Thinking Skills of Junior High School Students in Mathematics Learning. The research method used is quantitative descriptive using the quasi-experimental method. The results of this study obtained the Independent sample t-test test showing that the significance of 0.024 at the significance level α = 0.05, the two-sided p significance level of 0.024, which means less than 0.05, then H_a is accepted and H_0 is rejected, which means there is a statistically significant effect of the Inside Outside Circle cooperative learning model assisted by audiovisual media on critical thinking skills.

How does the training given to intensive care nurses affect their attitudes toward preventing pressure ulcers?

ObjectiveThe study aimed to determine the effect of training in the prevention/treatment of pressure ulcers on nurses' attitudes. Material methodThe study was conducted experimentally in a pretest-posttest design. Training was given to nurses participating in the study with content prepared by "Wound and Ostomy Incontinence Nurses Association" guide. The study was conducted between August and October with nurses working in Level 3 intensive care units. The study was completed with 79 nurses. Before the training was given, the "Nurse Introduction Form" and " Attitude Towards Pressure Ulcer Prevention Instrument (ATPUPI)" form were applied to those. All nurses were re-administered the ATPUPI scale three months after the training to evaluate their attitudes. Frequency analysis, descriptive statistics, one-way ANOVA, and independent sample tests were used in statistical analyses. ResultsThe mean age of the nurses was 29.62 ± 5.74 years, the mean length of service was 71.77 ± 77.19 months, 94.9 % were graduates, and 82.3 % worked in shifts. The mean scores before the training were 44.53 ± 4.13 and 45.12 ± 2.53 after the training. It was determined that there was a significant difference between the nurses who did not receive training on pressure ulcers other than undergraduate education and those who obtained training in terms of the mean score of ATPUPI. Still, after the training, the mean scores reached a similar level, and this difference disappeared. The increase in the mean scores of those who did not receive any education other than undergraduate education was found to be statistically significant. ConclusionAs a result of our study, intensive care nurses' attitudes toward preventing pressure ulcers were found to be at a high level. It was found that the training given to nurses who had not received training on pressure ulcers other than undergraduate education had a significant positive effect on their attitudes. According to this result, it is recommended that in-service training should be given from the moment they start intensive care and repeated at regular intervals to ensure continuity in knowledge, attitude, and care behavior.

Novel multifactor predictive model for postoperative survival in gallbladder cancer: a multi-center study

BackgroundGallbladder cancer (GBC) is a highly aggressive malignancy, with limited survival profiles after curative surgeries. This study aimed to develop a practical model for predicting the postoperative overall survival (OS) in GBC patients.MethodsPatients from three hospitals were included. Two centers (N = 102 and 100) were adopted for model development and internal validation, and the third center (N = 85) was used for external testing. Univariate and stepwise multivariate Cox regression were used for feature selection. A nomogram for 1-, 3-, and 5-year postoperative survival rates was constructed accordingly. Performance assessment included Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier curves were utilized to evaluate the risk stratification results of the nomogram. Decision curves were used to reflect the net benefit.ResultsEight factors, TNM stage, age-adjusted Charlson Comorbidity Index (aCCI), body mass index (BMI), R0 resection, blood platelet count, and serum levels of albumin, CA125, CA199 were incorporated in the nomogram. The time-dependent C-index consistently exceeded 0.70 from 6 months to 5 years, and time-dependent ROC revealed an area under the curve (AUC) of over 75% for 1-, 3-, and 5-year survival. The calibration curves, Kaplan-Meier curves and decision curves also indicated good prognostic performance and clinical benefit, surpassing traditional indicators TNM staging and CA199 levels. The reliability of results was further proved in the independent external testing set.ConclusionsThe novel nomogram exhibited good prognostic efficacy and robust generalizability in GBC patients, which might be a promising tool for aiding clinical decision-making.

Thyroid dysfunction prevalence in high-risk pregnant women and maternal and neonatal consequences after delivery: A retrospective cohort study

Objective: To evaluate thyroid dysfunction prevalence and its consequences in high-risk pregnant women and their children. Methods: In this retrospective cohort study, data from high-risk pregnant women who were referred to a governmental referral hospital in Qom, Iran from March to August 2022 were collected. The thyroid disorders were determined according to the 2017 American Thyroid Association guidelines. The predictors of a mother’s hypothyroidism and its fetal and maternal consequences were assessed. Independent t-test, Chi-square test, and logistic regression were used for bivariate and multivariate analysis. Results: 293 women were included. The prevalence of hypothyroidism and hyperthyroidism based on the thyroid stimulating hormone classification was 32.5% and 2.0%. The prevalence of subclinical and overt hypothyroidism was 6.1% and 4.1%, and the prevalence of subclinical and overt hyperthyroidism was 2.4% and 1.4%, respectively. Family history of thyroid disorders was the most important predictor of hypothyroidism during pregnancy (OR=2.6, 95% Cl=1.0-6.6, P=0.002), while preterm delivery (OR=2.2, 95% CI=1.3-6.8, P=0.220) and elevated neonate thyroid stimulating hormone (OR=1.2, 95% CI=1.1-1.3, P=0.041) were the most important consequence of hypothyroidism. Conclusions: Hypothyroidism is a highly prevalent thyroid disorder in high-risk pregnant women. Thyroid function screening should be performed in all pregnant women to increase the detection of subclinical and overt hypothyroidism during pregnancy.

Female sexual dysfunctions in multiple sclerosis patients with lower urinary tract symptoms: an Italian case-control study.

Multiple sclerosis (MS) is a recurrent, autoimmune, and inflammatory demyelinating chronic disease that typically manifests in young adulthood and exerts adverse effects on sexual functions. The study evaluated the prevalence of sexual dysfunctions (SDs) and the relationship with neurological disability, depression, and lower urinary tract symptoms (LUTS) in a cohort of MS female patients, comparing these results with those of healthy women. From January 2023 to January 2024, consecutive premenopausal female patients with MS, were recruited and the examination included urinalysis, ultrasonography and a urodynamic test according to the International Continence Society standard. Descriptive statistics were reported as mean and standard deviation for continuous variables (analyzed by independent samples Mann-Whitney U test and independent samples Kruskal-Wallis test) while categorical variables were reported as frequency and percentage (analyzed by chi-square test with Fisher's exact test). Female Sexual Function Index (FSFI) total score and all FSFI subscales scores were significantly lower in patients with MS vs healthy control subjects (P < .001); FSFI total scores and all FSFI subscale scores were statistically significantly lower in patients with MS with an International Prostate Symptom Score ≥20 (P < .001) and considering a cutoff for Beck Depression Inventory-II score ≥17, depression was present in 61% (n = 47 of 77) of patients with MS and completely absent in the control group. The knowledge that SDs are a common problem in MS and in other chronic illnesses can alleviate the feeling of stigma and talking openly of sexual problems can be helpful for the patients and so the doctor-patient relationship can be reinforced. The sample was drawn from a single center, and larger multicenter studies that include both genders are needed to obtain strong results. Our findings confirm the idea of a polygenic and multifactorial etiology of female SDs in MS. Therefore, women with MS should be evaluated in terms of SDs during follow-ups.

Short baseline neutrino anomalies at stopped pion experiments

Stopped-pion experiments that measure coherent elastic neutrino-nucleus scattering (CEνNS) are sensitive to sterile neutrinos via disappearance. Using timing and energy spectra to perform flavor decomposition, we show that the delayed electron neutrino component provides an independent test of short-baseline anomalies that hint at ~ eV-mass sterile neutrinos. Dedicated experiments will be sensitive to nearly the entire sterile neutrino parameter space consistent with short-baseline data.

Elastography-based AI model can predict axillary status after neoadjuvant chemotherapy in breast cancer with nodal involvement: A prospective, multicenter, diagnostic study

Objective: To develop a model for accurate prediction of axillary lymph node (LN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients with nodal involvement. Methods: Between October 2018 and February 2024, 671 breast cancer patients with biopsy-proven LN metastasis who received NAC followed by axillary LN dissection were enrolled in this prospective, multicenter study. Preoperative ultrasound (US) images, including B-mode ultrasound (BUS) and shear wave elastography (SWE), were obtained. The included patients were randomly divided at a ratio of 8:2 into a training set and an independent test set, with five-fold cross-validation applied to training set. We first identified clinicopathological characteristics and conventional US features significantly associated with the axillary LN response and developed corresponding prediction models. We then constructed deep learning radiomics (DLR) models based on BUS and SWE data. Models performances were compared, and a combination model was developed using significant clinicopathological data and interpreted US features with the SWE-based DLR model. Discrimination, calibration and clinical utility of this model were analyzed using receiver operating characteristic curve, calibration curve and decision curve, respectively. Results: Axillary pathologic complete response (pCR) was achieved in 52.41% of patients. In the test cohort, the clinicopathologic model had an accuracy of 71.30%, while radiologists’ diagnoses ranged from 64.26% to 71.11%, indicating limited to moderate predictive ability for the axillary response to NAC. The SWE-based DLR model, with an accuracy of 80.81%, significantly outperformed the BUS-based DLR model, which scored 59.57%. The combination DLR model boasted an accuracy of 88.70% and a false-negative rate of 8.82%. It demonstrated strong discriminatory ability (AUC, 0.95), precise calibration (p value obtained by Hosmer–Lemeshow goodness-of-fit test, 0.68), and practical clinical utility (probability threshold, 2.5-97.5%). Conclusions: The combination SWE-based DLR model can predict the axillary status after NAC in patients with node-positive breast cancer, and thus, may inform clinical decision-making to help avoid unnecessary axillary LN dissection.

Machine learning model based on dynamic contrast-enhanced ultrasound assisting LI-RADS diagnosis of HCC: A multicenter diagnostic study

BackgroundTo enhance the accuracy of hepatocellular carcinoma (HCC) diagnosis using contrast-enhanced (CE) US, the American College of Radiology developed the CEUS Liver Imaging Reporting and Data System (LI-RADS). However, the system still exhibits limitations in distinguishing between HCC and non-HCC lesions. PurposeTo investigate the viability of employing machine learning methods based on quantitative parameters of contrast-enhanced ultrasound for distinguishing HCC within LR-M nodules. Materials and methodsThis retrospective analysis was conducted on pre-treatment CEUS data from liver nodule patients across multiple centers between January 2013 and June 2022. Quantitative analysis was performed using CEUS images, and the machine learning diagnostic models based on quantitative parameters were utilized for the classification diagnosis of LR-M nodules. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) and compared with the performance of four radiologists. ResultsThe training and internal testing datasets comprised 168 patients (median age, 53 years [IQR, 18 years]), while the external testing datasets from two other centers included 110 patients (median age, 54 years [IQR, 16 years]). In the internal independent test set, the top-performing Random Forest model achieved an AUC of 0.796 (95%CI: 0.729–0.853) for diagnosing HCC. This model exhibited a sensitivity of 0.752 (95%CI: 0.750–0.755) and a specificity of 0.761 (95%CI: 0.758–0.764), outperforming junior radiologists who achieved an AUC of 0.619 (95%CI: 0.543–0.691, p < .01) with sensitivity and specificity of 0.716 (95%CI: 0.713–0.718) and 0.522 (95%CI: 0.519–0.526), respectively. ConclusionSignificant differences in contrast-enhanced ultrasound quantitative parameters are observed between HCC and non-HCC lesions. Machine learning models leveraging these parameters effectively distinguish HCC categorized as LR-M, offering a valuable adjunct for the accurate classification of liver nodules within the CEUS LI-RADS framework.

IACP-DFSRA: Identification of Anticancer Peptides Based on a Dual-channel Fusion Strategy of ResCNN and Attention

Anticancer peptides (ACPs) have been widely applied in the treatment of cancer owing to good safety, rational side effects, and high selectivity. However, the number of ACPs that have been experimentally validated is limited as identification of ACPs is extremely expensive. Hence, accurate and cost-effective identification methods for ACPs are urgently needed. In this work, we proposed a deep learning-based model, named iACP-DFSRA, for ACPs identification. Specifically, we adopted two kinds of sequence embedding technologies, ProtBert_BFD pre-training language model and handcrafted features to encode protein sequences. Then, the LightGBM was used for feature selection, and the selected features were input into ResCNN and Attention mechanism, respectively, to extract local and global features. Finally, the concatenate features were deeply fused by using the Attention mechanism to allow key features to be paid more attention to by the model and make predictions by fully connected layer. The results of 10-fold cross-validation demonstrated that the iACP-DFSRA model delivered improved results in most metrics with Sp of 94.15%, Sn of 95.32%, Acc of 94.74% and MCC of 89.48% compared to the latest AACFlow model. Indeed, the iACP-DFSRA model is the only model with Acc > 90% and MCC > 80% on this independent test dataset. Furthermore, we have further demonstrated the superiority of our model on additional datasets. In addition, t-SNE and SHAP interpretation analysis demonstrated that it is crucial to use two channels for feature extraction and use the Attention mechanism for deep fusion, which helps the iACP-DFSRA to predict ACPs more effectively.

Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon

This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon (SOC). Accordingly, two SOC modeling approaches were used in three agricultural sites in Czech Republic: i) machine learning (ML) including partial least squares regression (PLSR), cubist, random forest (RF), and support vector regression (SVR), and ii) regression kriging (RK) by the combination of ordinary kriging (OK) and PLSR (PLSR-K), cubist (cubist-K), RF (RF-K), and SVR (SVR-K). Models were developed on environmental predictor covariates (EPCs) and thirty genetic algorithms (GA)-selected visible, near-infrared, and shortwave-infrared (VNIR–SWIR) wavelengths spectra, individually and combined. Thirty rasters were then created using interpolation of the selected spectra and served as the input variables – with and without EPCs – to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach: iii) kriging using OK of the measured and ML-predicted SOC. The impact of employing selected wavelengths’ spectra and EPCs on models' performance was investigated using independent test samples and the uncertainty associated with the produced maps. Using interpolated spectra as the only input variable yielded a relatively acceptable accuracy (Nová Ves: RMSE = 0.19%, Údrnice: RMSE = 0.12%, Klučov: RMSE = 0.13%). In comparison, the interpolated spectra coupled with EPCs enhanced the results. Regarding the uncertainty, however, the ML-based SOC maps were more reliable, than RK-based ones. Furthermore, maps produced using both spectra and EPCs showed less uncertainty than those constructed on the individual datasets.

Improving Problem-Solving Skills Using ESD Integrated PBL Models Assisted with the Greenhouse Limas Project

Improving Problem-Solving Skills Using ESD Integrated PBL Models Assisted with the Greenhouse Limas Project. Problem-solving skills in high school students are still low, there are still many students who have not been able to develop problem-solving so innovative learning models are needed in learning at school. This study aims to describe the implementation of the ESD-integrated PBL model utilizing greenhouse lime to improve students' problem-solving skills. The type of research used in the research is this research using the type of Quasi-Experimental Design research. The participants in this study are students of class X Mipa at one of the high schools in Pamekasan Regency. This study uses 2 classes, namely the control class and the experimental class. Based on the results of the research, it was concluded that the ESD-integrated PBL model learning activities to improve the solving skills of class X students were carried out with an average percentage of 95.55% in the very good category. The application of ESD-integrated PBL model learning can improve students' problem-solving skills. This is characterized by 1) the results obtained from the post-test score are higher than the results of the pre-test with an N-Gain of 0.78 in the high category, while in the control class, the results of the N-Gain obtained are 0.67 with the medium category 2) the results of the post-test of the two classes in the independent test are 0.162 which means that the significant value is greater than 0.05, then H1 is accepted. Thus, there was a significant difference in the mean post-test scores of the experimental class and the post-test of the control class. Students' responses to the application of ESD-integrated PBL model learning to improve students' problem-solving skills based on the analysis of the response questionnaire obtained a positive response in the experimental class with an average percentage above 70%, which means that the points on the student response questionnaire were in a good category.

The Impact of Science, Environment, Technology, and Society (SETS) Learning Model Integrated with Google Earth on Spatial Thinking Ability in Senior High School

Developing spatial thinking skills in students often poses a challenge for Geography teachers in Indonesia. The concept of spatial relationships in geography places spatial thinking as a distinct aspect of intelligence, enabling individuals to identify patterns and trends in spatial change. Therefore, appropriate models and strategies are needed to develop spatial thinking skills. This study aims to determine the effect of the Science, Environment, Technology and Society (SETS) learning model integrated with Google Earth on students' spatial thinking ability. This research is a quasi-experiment with posttest-only design nonequivalent groups that uses a quantitative approach with the inferential statistical method using the Independent sample t-test parametric test. The subjects of this study were randomly selected XI grade students from SMAN 10 Malang including, XI-C class as the experimental class and XI-D as the control class. The research instruments used are seven essay questions on spatial thinking ability. The data obtained will be analyzed using parametric statistics Independent sample t-test and obtained sig value. (2-tailed) 0.041. The mean value of the experimental class is greater (80.14) than the mean value of the control class (72.08). It can be concluded that the Science, Environment, Technology, and Society (SETS) model integrated with Google Earth affects students' spatial thinking ability on the material of Disaster and Environment class XI SMAN 10 Malang City. Another finding of this study is that three syntaxes are dominant in bringing up and developing students' spatial thinking ability, namely initiation, concept development formation, and concept application.

Exploring the Impact of MBTI Personality Types on Teaching Methods

The importance of personality traits impacting teaching practices is comprehending how teachers at different educational system levels treat learners, develop curricula, adopt applications and technologies, and give feedback. This research studies the influence of the correlation between the MBTI personality types of educators in the University of Swat and their teaching preferences, in addition to identifying educators’ learning style preferences connected to organized and flexible lesson planning, and active and reflective teaching. Under a quantitative research design approach, a structured questionnaire was completed by 50 teachers and associated staff. MBTI was used to determine participants' personality types with the researcher having established a Teaching Preferences Questionnaire to determine the participants’ perception about various teaching and learning styles. The feedback provided was made using a 5-point Likert scale and data analysis was done using SPSS version 27.0. To test the correlation between the various teaching preferences and the MBTI personality types, a set of statistical tools which included independent T-tests, ANOVA, Chi-Square tests, logistic regression, and Pearson correlation test were employed. This study revealed a significant relationship between personality traits and teaching behaviors. Teachers with relatively greater levels of feeling-oriented and intuitive characteristics were observed to adopt a dynamic approach to teaching which involved students incorporated concepts such as teaching involving students and had greater empathy and concern towards students. It was also found that students with certain personality types are more willing to adopt different technologies for enhancing the processes of teaching and learning. The study reveals a profound influence of the personality traits formulated by the MBTI on the choices of educators in teaching and the real methods applied. More specifically, the features of being feeling-oriented, intuitive, and judging predetermine the outlook on approaches to Active Teaching, structured lesson planning, application of technology in the learning process, and feedback methods.

PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE SNOWBALL THROWING DENGAN MEDIA KONTEKSTUAL TERHADAP KETERAMPILAN BERPIKIR KRITIS DAN KETERAMPILAN SOSIAL SISWA KELAS V PADA PEMBELAJARAN IPS

This research aims to describe the impact of implementing the snowball throwing cooperative learning model with contextual media on the critical thinking skills and social skills of fifth-grade students. The study was conducted at SDN Sawaran Kulon 02 in Kecamatan Kedungjajang Kabupaten Lumajang. This research employs a quantitative approach using quasi-experimental research with a nonequivalent control group design. Two classes were involved class VA served as the experimental group, while class VB served as the control group. The research instruments used were social skills observation sheets and learning outcome tests. The analysis techniques included descriptive analysis, normality tests, paired sample t-tests, homogeneity tests, independent samples t-tests, N-Gain score tests, and independent t-tests for N-Gain scores. The results from the independent samples test yielded a significance value (2-tailed) of 0.000, which is less than 0.05, indicating a significant difference in the average learning outcomes between the experimental and control classes. It can be concluded that the implementation of the snowball throwing cooperative learning model with contextual media has a significant effect on students' critical thinking skills and social skills.

Pengaruh Media Pembelajaran Geometri Bangun Ruang Berbasis Google Sites Terhadap Hasil Belajar Kelas V

The purpose of this research is to determine whether there is an influence of Google Sites-based spacial geometry learning media on the learning outcomes of class V students. This type of research is quasi-experimental with a Nonequivalent Control Group Design. The population in this study were all fifth grade students at SDN Bugih 1 Pamekasan. The sampling technique used Simple Random Sampling, with a sample of 44 students. Research data was collected using learning outcomes tests, observations of learning implementation, and student response questionnaires. The research results showed that the average learning outcome for the control class after being treated was 0.00 and the average learning outcome for the experimental class after being treated was 83.33. Then the results of the independent sample t-test hypothesis test from student learning outcomes after being given treatment obtained a significance value of 0.000 and tcount &gt; ttable, namely 4.234 &gt; 2.021. Based on decision making in the independent sample t-test, if the significance value is &lt;0.05 and tcount &gt; ttable, then Ho is rejected and Ha is accepted, that is, there is a significant difference between the learning outcomes of the control class and the experimental class, and the learning outcomes of the class. The experiment was better than the control class learning results. So it can be concluded that there is a significant difference in average learning outcomes between the control class and the experimental class, there is an influence of Google Sites-based spatial geometry learning media on the learning outcomes of class V students at SDN Bugih 1 Pamekasan

The Influence of Women’s Empowerment on The Preference for Contraceptive Methods in Indonesia: A Multinomial Logistic Regression Modelling

The concept of women's empowerment encompasses enabling women to take control of their own lives, independently make choices, and fulfill their complete capabilities. Numerous research studies examined the correlation between the empowerment of women and their reproductive health. In Indonesia, female labor force participation is relatively low. As a result, research on the influence of empowering women on contraceptive method preference in Indonesia makes sense. This research aims to find the multinomial logistic regression model in choosing contraceptive methods for married women in Indonesia and to identify the women’s empowerment traits that most impact contraceptive method choice. For this study, the researchers utilized secondary data obtained from the 2017 Indonesian Demographic and Health Survey (IDHS). The participants consisted of women between the ages of 15 and 49 who were married. The total number of respondents sampled was 49,216. Variables that significantly affect contraceptive method use include the respondent's current employment, the respondent has bank account or other financial institution accounts, the cumulative count of offspring previously born and beating justified if the wife argues with her husband. The analysis is obtained using the multinomial logistic regression test, independency, multicollinearity, and parameter test, and the selection is made by considering either the smallest value of Akaike's information criterion or the option that achieves the highest level of accuracy. Findings highlight four significant variables: Firstly, employed women are more likely to use contraceptives than the unemployed. Secondly, access to banking services correlates with a higher likelihood of contraceptive use. Thirdly, women with more children tend to prefer long-acting reversible contraceptives. Lastly, endorsement of spousal violence justifiability is linked to conventional contraceptive selection. These results emphasize the roles of employment, financial access, family size, and gender-based violence perceptions in shaping contraceptive choices in Indonesia. Model 3 emerges as the most accurate predictor of preferences after eliminating six variables based on rigorous testing and multicollinearity considerations. These findings underscore the importance of addressing economic empowerment and gender-related issues in Indonesian reproductive health programs and policies. Such a comprehensive approach can enhance women's autonomy, enabling them to make crucial life choices and ultimately improving their overall well-being.

Impact of Social Media Use, Social Comparison &amp; Self-Esteem Among Adolescents

The present research examined how social media, social comparison, and self-esteem affect adolescents. After an extensive literature review, these hypotheses were developed. 1) Social media use, social comparison, and self-esteem among teenagers will be significantly related. 2) Social media use will affect self-esteem among adolescents.3) Adolescent self-esteem will be affected by social comparison.4) Gender will affect adolescent social media use, comparison, and self-esteem of the 249 samples, 153 are female and 96 are male. Survey participants were 14–25 years old. Data were purposively sampled from Faisalabad educational institutions. The findings were assessed using these methods. Participant demographics were collected using a sheet. Adolescent self-esteem was measured with a scale. The Social Comparison scale measured self-comparative behavior. Participants' social media use was measured using a scale. Analyze the data using correlation, regression, and independent sample tests. Findings illuminate individual traits and tendencies.

A Comprehensive Evaluation of Machine Learning on Coral Trace Element Paleothermometers for Sea Surface Temperature Reconstruction

AbstractThis research introduces a novel approach to reconstruct sea surface temperature (SST) by developing a universal coral thermometer using machine learning (ML) algorithms on monthly resolved Porites coral proxies and SST data. A total of 1,202 data sets from 19 corals, covering SSTs ranging from 21.5 to 31.5°C, with proxies including Sr/Ca, Mg/Ca, Li/Mg, U/Ca, and B/Ca ratios were analyzed. The data were divided into four sub‐datasets by regional and taxon constraints. An exhaustive analysis was conducted, training 1,612 models using various proxy combinations and ML strategies to assess the impact of the non‐SST effect on the universality of ML models. The results indicated that the non‐SST effect is more significantly attributed to regional variations than to taxon differences, underscoring the importance of regional factors in Porites coral proxy‐based SST reconstructions. Sr/Ca and Li/Mg proxies were identified as the most indicative of SST, showing clearer relationships with temperature than other proxies. Non‐linear approaches achieved a Root Mean Square Error (RMSE) of less than 0.90°C, which further decreased to 0.72°C upon incorporating specific regional and taxon constraints. In an independent test set focusing exclusively on Li/Mg and Sr/Ca proxies, the tree‐based algorithms particularly excelled, achieving an average RMSE improvement of at least 0.52°C over the Universal Multi‐Trace Element Calibration Scheme and the Li/Mg empirical equation. This research underscores the potential of applying ML to coral‐based SST reconstructions, especially highlighting the effectiveness of tree‐based algorithms and the suitability of Sr/Ca and Li/Mg proxies for accurate temperature estimations.

Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.

During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) algorithms may be able to provide more accurate advice on the classification of COVID-19 patients, offering predictive, preventive, and personalized medicine (PPPM) solutions in the future. In this retrospective study, we divided a portion of the data into training and validation cohorts in a 7:3 ratio and established a model based on a combination of two ML algorithms first. Then, we used another portion of the data as an independent testing cohort to determine the most accurate and stable model and compared it with other scoring systems. Finally, patients were categorized according to risk scores and then the correlation between their clinical data and risk scores was studied. The elderly accounted for the majority of hospitalized patients with COVID-19. The C-index of the model constructed by combining the stepcox[both] and survivalSVM algorithms was 0.840 in the training cohort and 0.815 in the validation cohort, which was calculated to have the highest C-index in the testing cohort compared to the other 119 ML model combinations. Compared with current scoring systems, including the CURB-65 and several reported prognosis models previously, our model had the highest AUC value of 0.778, representing an even higher predictive performance. In addition, the model's AUC values for specific time intervals, including days 7,14 and 28, demonstrate excellent predictive performance. Most importantly, we stratified patients according to the model's risk score and demonstrated a difference in survival status between the high-risk, median-risk, and low-risk groups, which means a new and stable risk assessment system was built. Finally, we found that COVID-19 patients with a history of cerebral infarction had a significantly higher risk of death. This novel risk assessment system is highly accurate in predicting the prognosis of patients with COVID-19, especially elderly patients with COVID-19, and can be well applied within the PPPM framework. Our ML model facilitates stratified patient management, meanwhile promoting the optimal use of healthcare resources.