Varying Sample Sizes Research Articles

Assessing the impact of hard data patterns on Bayesian Maximum Entropy: a simulation study.

This study empirically tested the robustness of Bayesian Maximum Entropy (BME) in predicting spatiotemporal data, with an emphasis on skewness, sample size, and spatial dependency level. Simulated data, both Gaussian and non-Gaussian, were generated using the unconditional sequential simulation method, with sample sizes ranging from 100 to 500 at the interval length of 50 and varying skewness (0, 1, 3, 6 and 9) and spatial dependency levels (weak, moderate, and strong). Findings revealed sample size variations and spatial dependence levels did not significantly influence BME prediction's Mean Square Error (MSE) and bias. While skewness significantly impacted MSE (p-value < 0.001), bias remained unaffected. Moreover, skewness and spatial dependence interactions affected both MSE and bias. Despite this, BME proved robust to sample size and skewness, demonstrating a negligible MSE on the graphical plot (heatmap).

Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study.

One of the most promising approaches for early and more precise disease prediction and diagnosis is through the inclusion of proteomics data augmented with clinical data. Clinical proteomics data is often characterized by its high dimensionality and extremely limited sample size, posing a significant challenge when employing machine learning techniques for extracting only the most relevant information. Although there is a wide array of statistical techniques and numerous analysis pipelines employed in proteomics data analysis, it is unclear which of these methods produce the most efficient, reproducible, and clinically meaningful results. In this study, we compared 9 unique analysis schemes comprised of different machine learning and dimensionality reduction methods for the analysis of simulated proteomics data consisting of 1317 proteins measured in 26 subjects (i.e., 13 controls and 13 cases). In scenarios where the sample size is extremely small (i.e., n < 30), all schemes resulted in an exceptionally high level of performance metrics, indicating potential overfitting. While performance metrics did not exhibit significant differences across schemes, the set of proteins selected to be discriminatory between groups demonstrated a substantial level of heterogeneity. However, despite heterogeneity in the selected proteins, their biological pathways and genetic diseases exhibited similarities. A sensitivity analysis conducted using varying sample sizes indicated that the stability of a set of selected biomarkers improves with larger sample sizes within a scheme. When the aim of the study is to identify a statistical model that best distinguishes between cohort groups using proteomics data and to uncover the biological pathways and disorders common among the selected proteins, the majority of widely used analysis pipelines perform similarly. However, if the main objective is to pinpoint a set of selected proteins that wield significant influence in discriminating cohort groups and utilize them for subsequent investigations, meticulous consideration is necessary when opting for statistical models, due to the possibility of heterogeneity in the sets of selected proteins.

Physical and occupational therapy service delivery models for populations identified as hard-to-reach: A scoping review.

The delivery of rehabilitation services for hard-to-reach populations (e.g., refugees) is highly complex. There is a need for evidence-based approaches to deliver physiotherapy (PT) or occupational therapy (OT) services to this underserved group. The purpose of this scoping review was to identify PT and OT service delivery models that have been implemented, for populations typically identified as hard-to-reach and their associated health outcomes. Articles were eligible if they described PT and/or OT services for hard-to-reach populations. There were no restrictions on study design. Six electronic databases (AMED, CINAHL, MEDLINE, EMBASE, Healthstar, and PsycINFO) were searched from January 2000 to June 2023. Articles were screened in duplicate by two independent reviewers, and conflicts were resolved by consensus. Twenty-one articles with variable sample sizes (min, max n = 3 to 237) were included and detailed PT and/or OT services for immigrants/migrants, refugees, hard-to-reach veterans, people experiencing homelessness, lower incomes, trauma/torture, and those living in rehabilitation-deficient areas. Common rehabilitation needs (e.g., clinician to client connectivity), barriers (e.g., high transportation costs) and facilitators (e.g., encouragement) were identified among the various populations, mainly due to intersecting identities such as those who are both traumatized and refugees. Unique factors pertaining to the PT and OT services were also identified in some groups, including access to child and family services for people experiencing homelessness. Despite common and individual needs, barriers, and facilitators in hard-to-reach groups in the literature, there is a need for studies with larger sample sizes, rigorous methodology and a conscious effort to publish the results of interventions to generate stronger recommendations for practice.

Evaluation of ploidy and the DNA index by flow cytometry in central nervous system tumors: a review.

Research on central nervous system tumors (CNSTs) has a significant impact on the diagnosis and prognosis of patients. Currently, CNSTs are classified according to the schema proposed by the World Health Organization (WHO), which considers clinical, histopathological, and molecular characteristics, highlighting the importance of tumor biology for accurate diagnosis and optimal treatment approaches. Despite these advances, assessing DNA ploidy-a marker of tumor aggressiveness-remains complex in CNSTs. This review investigates the utility of DNA index (DNAi) and DNA ploidy analysis by flow cytometry in diagnosing CNSTs and prognosing their outcomes. We systematically reviewed studies in the PubMed database from 1990 to the present using the keywords "DNA Index", "Brain", "Flow cytometry", and "Ploidy". We identified 151 studies, 36 of which met our inclusion criteria. We found considerable variation in sample sizes and methodological variation across the studies. Discrepancies between the reported DNAi and ploidy values were observed. Aneuploidy is generally associated with more aggressive tumors, although exceptions exist. Higher DNAi levels correlate with increased malignancy, notably in glioblastomas, astrocytomas, and meningiomas, whereas diploid astrocytomas and oligodendrogliomas are associated with shorter survival rates. DNA ploidy assessment via flow cytometry could predict CNST behavior, yet methodological issues with tissue selection, adequate control samples, and technique variability remain. DNAi and ploidy assessments show promise as prognostic markers in CNSTs. However, the standardization of flow cytometry protocols and alignment with the current WHO classification schema are essential steps to integrate ploidy analysis in routine CNST assessment.

A new Marshall-Olkin generalized-k family of distributions with applications

Abstract In this article, we describe a new Marshall-Olkin family of distributions based on the Marshall-Olkin family of distributions by employing a different generator. We have derived several statistical properties of the new Marshall-Olkin family, including survival function, hazard rate function, quantile function, moments, and linear representation. We have taken the Weibull distribution as a baseline distribution and found that the new Marshall-Olkin Weibull distribution exhibits a variety of shapes for its hazard rates and densities. We have tested the performance of various estimation methods for the new Marshall-Olkin family of distributions, including eight different estimation methods. Additionally, we conducted extensive simulations to assess the robustness of the estimation methods across various scenarios and determine which methods are most effective for varying sample sizes. To further validate the new Marshall-Olkin Weibull distribution, we have used two different data sets to compare the fitted new Marshall-Olkin Weibull distribution with the well-known comparative models. By testing the performance of different estimation methods and using multiple data sets, we can demonstrate the versatility and usefulness of the new Marshall-Olkin family of distributions for modeling a wide range of phenomena.

Molecular dynamics simulation study to investigate electrical properties of plumbene

ABSTRACT Plumbene, the new 2D structure of lead, single-layered with a hexagonal arrangement of atoms draws the attention of researchers around the globe. We have designed a plumbene sheet using multi-scale modelling i.e. nonequilibrium molecular dynamics (NEMD) simulation methods to determine the electrical properties of plumbene. The effects of varying sample sizes and temperatures on the electrical properties of plumbene using NEMD are studied here. It is observed from 298 K to 318 K electrical conductivity of plumbene ranges from 2.7792 × 10−14 S/m to 1.2012 × 10−13 S/m, with a dwindling nature. Almost static electrical resistivity from 298 K to 378 K is noticed, afterwards a sudden sharp increase till 398 K. The Lorenz number of plumbene from 298 K to 318 K shows reverse results with its electrical conductivity under similar experimental conditions. The electrical conductivity of plumbene sheets at 298 K with 600Å to 1050Å ranges from 0.17794 × 10−14 S/m to 4.7696 × 10−14 S/m. Only at 750Å sample size is a sharp jump is noticed. A reverse trajectory is observed for both electrical resistivity and Lorenz number of plumbene sheet samples. In these two cases, the sharp jump is observed at 1000Å. Our results will be helpful for the effective utilisation of plumbene in targeted applications of electrical engineering fields.

Integration of Calculator.oi Application in Improving Mathematics Learning Outcomes of Students in Elementary Schools

This study aims to evaluate the effectiveness of the Calculator.oi application integration in improving students' mathematics learning outcomes in Elementary Schools. This study focused on fourth-grade students at SDN 22 Ketapang and used a quantitative approach with an experimental method. In this study, the experimental method was applied to compare the differences in students' learning outcomes before and after using the Calculator.oi application. Learning outcome data were collected and analyzed using the Wilcoxon test, after the normality test showed that the data were not normally distributed. The results showed a significant increase in students' mathematics learning outcomes after using the Calculator.oi application. Analysis of the differences between pre-test and post-test scores revealed that all students experienced significant progress in their understanding and achievement in mathematics. The significance value obtained from the statistical test was below 0.05, indicating that this application had a significant positive impact and was effective in improving students' learning outcomes. Based on these findings, it is recommended that the Calculator.oi application be integrated more widely into the mathematics learning process in Elementary Schools. The use of this application is considered an innovative and effective learning medium in supporting the understanding of mathematical concepts. Further research is recommended to involve a larger sample size and different class variations in order to obtain more comprehensive and representative results. In addition, exploration of students' perceptions regarding the use of this application as a learning tool is also important. This will provide a deeper understanding of how the Calculator.oi application can be accepted and utilized in a broader learning context.

Sexual harassment among higher education students: a worldwide review

Abstract Introduction Sexual harassment is an uprising problem in higher education systems worldwide with major impacts on students. The aim of this study was to describe and meta-analyze quantitative data on sexual harassment prevalence among higher education students. Methods We searched in seven databases and included studies published in English and French from 2000 until 2023 if they measured sexual harassment prevalence on students attending higher education, and they were quantitative cross-sectional studies. Prevalence estimates disaggregated by gender identity, and world region were meta-analyzed using a random-effects model and reported following PRISMA guidance. Results We identified 53 articles, from 6 different regions of the world, with highly variable sample sizes, ranging from 164 to 779,170. 45% of the samples were from the United States. 32% of studies used convenience or purposive sampling and 39% random sampling. The meta-analyzed prevalence of sexual harassment was globally 36.9% (95% CI [30.6, 43.6], 43.7% for women, 16.8% for men, 33.4% for non-binary. In additional, variations in prevalence were observed across the different world regions. In particular, the Eastern Mediterranean region presented the highest prevalence estimates for sexual harassment among women, reaching 67.5%, while the Americas region presented the highest prevalence estimates for sexual harassment among men, at 18.8%. Discussions Higher education institutions, especially in low- and middle-income countries, should commit to the conduct of surveys to monitor the prevalence of sexual harassment, and policymakers should allocate specific funds and resources to support the implementation of sexual harassment prevention and intervention initiatives in higher education, prioritizing the safety and well-being of students. Key messages • High prevalence of sexual harassment in higher education underscores urgent need for proactive measures to ensure student safety and well-being. • Policymakers should allocate specific funds and resources to support the implementation of SH prevention and intervention initiatives in higher education.

Bayesian control chart using variable sample size with engineering applications

In this study, we suggested an innovative approach by introducing an Adaptive Exponential Weighted Moving Average (AEWMA) control chart utilizing Variable Sample Size (VSS) under Bayesian methodology. The proposed methodology utilized an integer linear function to dynamically adjust sample sizes according to the AEWMA statistic. Another appealing feature of our adaptive framework is the integration of the smoothing constant of an EWMA chart, which enhances monitoring responsiveness. We reveal the superiority of our recommended control chart by extensive simulations to existing Bayesian EWMA and Bayesian AEWMA control charts using Fixed sample size (FSS). The offered Bayesian VAEWMA control chart is more sensitive to detection improvement, a decrease in the false alarm rate, and overall more effective than the existing methods. These findings provide additional justification for the basic notion that process control statistical tools needed to be dynamic, as the manufacturing process itself was dynamic. The results suggest the importance of introducing adaptive SPC methods in dynamic manufacturing environments. A real data application is performed to evaluate the validity and optimal performance of our recommended chart."Please check article if captured correctly."="Dear Editor we have checked and found corrrect."As per standard instruction, city is required for affiliations; however, this information is missing in affiliations [1, 5]. Please check if the provided city is correct and amend if necessary."Dear Editor we have checked and found correct. thanks youPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary."Dear Editor we have checked and found correct. thank you"

Update/Refinement of Targeted Muscle Reinnervation Indication: A Scoping Review of Applications for Non-Amputees

Background: Targeted muscle reinnervation (TMR) was originally developed to enhance prosthetic control in amputees. However, it has also serendipitously demonstrated benefits in reducing phantom pain and neuromas. As a result, it has emerged as a secondary treatment for chronic neuromas in amputees and holds promise for managing neuropathic pain in non-amputee patients, particularly those with neuromas. This review synthesizes the current literature on TMR indications for non-amputee patients, highlighting its potential to address chronic peripheral nerve pain and neuromas beyond its original application in amputation. Methods: A thorough search of the PubMed and Cochrane databases up to January 2024 was conducted following the PRISMA guidelines. Inclusion criteria comprised case series, cohort studies, and randomized controlled trials reporting TMR outcomes in non-amputees. Results: Of 263 articles initially identified, 8 met the inclusion criteria after screening and full-text assessment. The articles were all case series with varied sample sizes and mainly focused on neuroma treatment (n = 6) and neuropathic pain management (n = 2) for both upper and lower extremities. Clinical studies included TMR efficacy for sural nerve neuromas in the lower extremities and hand neuromas, showing pain relief and improved function. Key findings were encouraging, showing successful pain relief, patient satisfaction, and psychosocial improvement, with only rare occurrences of complications such as motor deficits. Conclusions: In non-amputee patients, TMR appears to be a promising option for the surgical management of neuropathic pain, demonstrating favorable patient satisfaction and psychosocial outcomes along with low morbidity rates. Although functional improvements in gait recovery and range of motion are encouraging, further research will be important to confirm and expand upon these findings.

Intelligent diagnostic method for beam pumping units based on torque indicator cards

An innovative approach is introduced to the intelligent diagnosis of torque indicator card conditions using electric parameters, effectively addressing the high costs associated with acquiring polished rod indicator cards and the convergence difficulties encountered when converting electrical parameters. The proposed method begins by establishing a library of 15 typical operating conditions, followed by an engineering feature analysis. Key to this approach is the use of the AlexNet model for intelligent recognition training, which successfully enhances model convergence and stability, even with varying sample sizes. This allows for automated recognition and classification of operating conditions through model training. The research outcomes demonstrate that the model achieved a fault diagnosis accuracy of over 95% for beam pumping units. This not only improves the accuracy and efficiency of fault diagnosis in oil well pumping units but also presents a novel and effective approach for intelligent diagnostic applications in oil field extraction, offering substantial practical value.

Determinants of Solar Home Systems Adoption among Riverine Island Dwellers: A Case Study of Sirajganj District in Bangladesh

Purpose: This study aims to identify the determinants of solar home systems adoption among riverine island dwellers, focusing on the Sirajganj district in Bangladesh. Methodology: The research, based on a comprehensive survey of 200 households, utilized logistic regression analysis and descriptive statistics. Findings: The study finds that ‘education of the household head’, ‘access to credit’, and ‘monthly household income’ significantly affected solar home systems adoption at different level of significance. Factors leading to adopt solar home systems included lack of electricity access, focus on energy security, and cost savings, along with access to loans or installments and community connections. Conversely, non-adopters faced barriers such as perceived high initial costs, lack of installment facilities, and insufficient awareness. Practical Implications: The study highlights the importance of credit accessibility, educational background, and household income in promoting solar home systems adoption. Policymakers and stakeholders can use these insights to tailor interventions addressing specific determinants and encourage widespread adoption. Originality/Value: This research provides valuable new insights into promoting sustainable energy initiatives in similar contexts. Limitations: The study's limitations include its small sample size and potential regional variations. Further research is recommended to validate and extend these findings across diverse geographical settings.

The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data

This research aims to explore and compare several nonparametric regression techniques, including smoothing splines, natural cubic splines, B-splines, and penalized spline methods. The focus is on estimating parameters and determining the optimal number of knots to forecast cyclic and nonlinear patterns, applying these methods to simulated and real-world datasets, such as Thailand’s coal import data. Cross-validation techniques are used to control and specify the number of knots, ensuring the curve fits the data points accurately. The study applies nonparametric regression to forecast time series data with cyclic patterns and nonlinear forms in the dependent variable, treating the independent variable as sequential data. Simulated data featuring cyclical patterns resembling economic cycles and nonlinear data with complex equations to capture variable interactions are used for experimentation. These simulations include variations in standard deviations and sample sizes. The evaluation criterion for the simulated data is the minimum average mean square error (MSE), which indicates the most efficient parameter estimation. For the real data, monthly coal import data from Thailand is used to estimate the parameters of the nonparametric regression model, with the MSE as the evaluation metric. The performance of these techniques is also assessed in forecasting future values, where the mean absolute percentage error (MAPE) is calculated. Among the methods, the natural cubic spline consistently yields the lowest average mean square error across all standard deviations and sample sizes in the simulated data. While the natural cubic spline excels in parameter estimation, B-splines show strong performance in forecasting future values.

Soil sampling design matters - Enhancing the efficiency of digital soil mapping at the field scale

Optimisation of sampling design (methods chosen to select the samples) and sample size (number of samples) remains a key challenge in digital soil mapping, especially in the area of precision farming with the expected economic benefits from the introduction of new technologies. As the existing information is available in the form of relevant environmental covariates, its combination with non-parametric machine learning techniques requires careful planning from the initial field sampling to the final production of digital soil maps. The aim of this study is to compare widely used covariate-wise sampling designs combined with variable sample sizes for supervised prediction of common soil drivers of agricultural productivity (pH, soil organic carbon, soil macronutrients) in a real case study of a field (35 ha) with heterogeneous soil properties. From a total of 200 samples, we evaluated different sample sets where 10, 30 and 60 field samples were selected by conditioned Latin Hypercube Sampling (cLHS) and Feature Space Coverage Sampling (FSCS) to calibrate random forest (RF) models. The evaluation was performed on independently in-situ sampled test points. In addition to these datasets, we also compared the investigated methods with Simple Random Sampling (SRS) in a numerical benchmark experiment with increasing sample size, comparing the global accuracies of the predicted maps on the test points, but using interpolated maps as the artificial true population for each soil characteristic. The results of the study in both the field experiment and the numerical experiment showed slightly better results for the FSCS method, especially when the number of samples was small. At smaller training sample sizes, the risk of insufficiently accurate prediction models was slightly lower for FSCS and the difference decreased as the sample size increased. Nevertheless, sample size proved to be the most important factor in the accuracy of RF models, regardless of the sampling technique. The results suggest that a sample size between 18 and 30 training samples (0.6 to 1 sample ha−1) seems plausible for covariate-wise predictions using RF at field scale in our case study. The relative importance of each auxiliary variable for each RF calibration was also assessed for the field experiment. The results showed that the additional introduction of spatial proxies overshadowed the importance of other covariates, but only significantly improved the model calibration at larger sample sizes. The calibrated models without spatial proxies showed the strongest effect of remotely sensed surface characteristics.

Methotrexate, Tofacitinib, and Biologic Disease-Modifying Antirheumatic Drug Safety and Effectiveness Among Patients with Rheumatoid Arthritis in Japan: CorEvitas Registry Observational Study.

The evolution of disease-modifying antirheumatic drugs (DMARDs) for the treatment of rheumatoid arthritis (RA) has improved patient prognosis. However, more real-world safety/effectiveness data comparing methotrexate (MTX), tofacitinib, tumor necrosis factor inhibitors (TNFi), and non-TNFi biologic DMARDs (bDMARDs)are warranted. The CorEvitas RA Japan registry was used to identify patients with rheumatologist-diagnosed RA who initiated MTX/tofacitinib/TNFi/non-TNFi bDMARDs. Safety outcomes included incidence of major adverse cardiovascular events (MACE), total cardiovascular disease, total serious infections, total herpes zoster, and total malignancies (excluding non-melanoma skin cancer). Effectiveness outcomes included change from baseline (Δ) in Clinical Disease Activity Index (CDAI) and proportion of patients achieving a minimum clinically important difference (MCID) in CDAI at month6. Adjusted regression models were fit; marginal means were estimated. Overall, 1972 patients were included in the safety cohort: MTX (N = 298); tofacitinib (N = 253); TNFi (N = 663); non-TNFi (N = 758). Mean follow-up time was 3.8, 2.9, 3.0, and 2.9years for MTX, tofacitinib, TNFi, and non-TNFi, respectively. Adjusted incidence rates (IRs, patients with events/100 patient-years [95% confidence intervals]) for MACE and total cardiovascular disease, respectively, were numerically lower for MTX (0.34 [0, 0.83]; 0.42 [0, 0.92]) and TNFi (0.09 [0, 0.27]; 0.61 [0.15, 1.07]) versus tofacitinib (0.48 [0, 1.20]; 2.30 [0.38, 4.22]) and non-TNFi (0.77 [0.35, 1.19]; 1.28 [0.73, 1.82]). Serious infections were numerically higher for non-TNFi (4.47 [3.38, 5.56]); herpes zoster was higher for tofacitinib (7.41 [4.52, 10.29]), versus other groups. IRs for malignancies were comparable between groups. Mean ΔCDAI and rates of achieving MCID in CDAI at month6 were generally greater with tofacitinib versus other groups. Some variations in incidence of safety outcomes were observed between treatments, while certain effectiveness outcomes favored tofacitinib. Sample size variation between groups and low number of safety events limited the analysis. Further studies are warranted to investigate observed differences. GOV: NCT05572567.

Chemical sample preparation of plant materials in tunnel-type microwave digestion systems for elemental analysis

Analyzing plant materials is essential for environmental monitoring, analytical control of food products, and medicinal raw materials. A review of global practices has shown that there are still no standard proceedings for chemical sample preparation suitable for all plant types without restrictions on the range of elements determined. Creating a standardized scheme for plants is feasible, as the macro composition of any plants consists of at least 90% organic compounds (cellulose, protein, lipids, etc.), whose mineralization results in the formation of water and a gaseous phase. In this study, certified plant samples were mineralized in a tunnel-type microwave digestion system MultiVIEW (SPC SCIENCE, Canada) with variations in analytical sample sizes, composition and volume of reagents, options for adding the reaction mixture, and vessel heating modes for simultaneous determination of a wide range of elements using inductively coupled plasma atomic emission spectrometry. The completeness of dissolution (the degree of correspondence between found and certified contents) was used as one of the criteria for the optimality of sample preparation conditions. It was shown that with a three-stage heating regime of the vessels (heating rate at the first stage 2.76 оC/min) with a sample weight of 0.5 g and separate sequential addition of the reaction mixture (4 ml HNO3, 1.5 ml H2O2, 1 ml HCl, and 0.05 ml HF), it is possible to reliably determine typical plant contents of Si, Al, Mg, Ca, Fe, Na, K, Ba, Sr, Rb, P, B, Mn, Ti, Ni, V, Cu, Zn.

Broadening the scope of food quality control: A calibration transfer strategy for minimizing external influences on spectral analysis

Calibration transfer (CT) is an effective tool to address spectral inconsistencies and the decline in model prediction capabilities caused by various influences. This study introduced a modified semi-supervised parameter-free calibration enhancement (MSS-PFCE) strategy to enable calibration models extensively applicable across varying temperatures, instruments and orientations. Investigations conducted on three datasets of watermelon juice, corn, and apples were utilized to validate the efficacy of MSS-PFCE. The results showed that MSS-PFCE decreased the average RMSEP for slave spectrum predictions by 96.51%, 75.96%, and 23.04%, respectively. MSS-PFCE significantly outperformed the original SS-PFCE approach and three classical standardization methods (direct standardization (DS), piecewise direct standardization (PDS) and slope and bias correction (SBC)). Furthermore, MSS-PFCE required no standards and needed only 5%–10% of slave samples for CT, maintaining robust reliability across varying sample sizes and cost thresholds (cth). The conclusions were further examined in three additional datasets with these variations. Notably, MSS-PFCE exhibited high accuracy, fitting stability, low sample dependency, and versatile transferability, positioning it as a promising candidate for widespread implementation in visible/near-infrared (Vis/NIR) spectroscopy. This study has significant potential to enhance the applicability of quality analysis, broaden the scope of food quality control, and promote the sound development of the food industry.

Role of Spinal Surgery Drainage Techniques in Postoperative Outcomes: Insights From a Comprehensive Literature Review.

Postoperative management often demands the introduction of several strategies in an attempt to minimize complication rates. One of the routine strategies includes the use of spinal drains, which have been questioned for their efficacy in improving postoperative outcomes. However, its role in postoperative outcomes is still debated. In general, this elucidation of an extensive literature review supports the synthesis of current evidence regarding the role of spinal drains in infection rates, hematoma formation, and overall patient recovery. A comprehensive search of PubMed from 2000 to 2024 was performed, focusing on studies investigating the use of spinal drains in spinal surgeries and their associated postoperative outcomes. It followed the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The inclusion criteria were studies related to spinal surgeries, excluding case reports, reviews, and editorials, and limited to articles published in English. Quality assessment was performed using the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool. A total of 19 studies were included, with different designs and varied sample sizes. The sample size was from 25 to 2,446 patients. Findings on infection rates were mixed; while one group of studies showed no significant differences in patients with and without drains, another group showed a reduced rate of reoperation for surgical site infections in patients with drains. In general, hematoma formation rates were reported to be the same across groups, while a few studies indicated that drains were more effective in managing wound exudates compared to no drains. Recovery outcomes indicated that patients who had a wound drain were more likely to stay in the hospital longer, although an improvement was noticed with time-driven wound drain removal, which resulted in shorter hospital stays and earlier ambulation. The use of spinal drains in postoperative spinal surgery presents both benefits and drawbacks. Spinal drains can assist in the management of wound exudates, and earlier detection of infection complications increases hospital stays and complications. Indeed, whether to use spinal drains or not should be an individual decision, weighing the potential benefits and risks. Future studies need to be done in order to establish clear guidelines for the use of drainage systems in various spinal surgical cases.

The Effectiveness of Mantel Haenszel Log Odds Ratio Method in Detecting Differential Item Functioning Across Different Sample Sizes and Test Lengths Using Real Data Analysis

Objectives: This study aims to determine the effectiveness of the Mantel Haenszel Log Odds Ratio method in detecting Differential Item Functioning (DIF) across gender, while considering variations in sample size and test length. Utilizing real data, the study draws from a sample of tenth-grade students in Jordan who participated in the 2018 PISA International Mathematics Test. Methods: The study employs the experimental methodology, utilizing three levels of sample size and test length: (342, 200, and 100) and (30, 20, and 10), respectively. Nine iterations of the DDFS program were conducted to collect the results, representing nine scenarios resulting from the intersection of sample size and test length levels. Results: The study indicates that variations in sample size and test length significantly affect the Mantel-Hanzel (MH) method. Specifically, it observes an improvement in the MH method’s ability to detect DIF items with larger sample sizes, while maintaining a consistent test length. Conversely, the method’s efficacy declines with longer test lengths, despite maintaining a fixed sample size at a specific level. Conclusion: The study recommends using a large sample size and a short test length for effective detection of DIF items using the MH method.

Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis

In landslide susceptibility assessment (LSA), inventory incompleteness impacts the accuracy of different models to varying degrees. However, this area remains under-researched. This study investigated six LSA models from heuristic, statistical, machine learning and ensemble learning models (analytical hierarchy process (AHP), frequency ratio (FR), logistic regression (LR), Keras based deep learning (KBDL), XGBoost, and LightGBM) across six different sample sizes (100%, 90%, 75%, 50%, 25%, and 10%). Results revealed that XGBoost and LightGBM consistently outperformed other models across all sample sizes. The LR and KBDL models followed, while FR model was the most affected by sample size variations. AHP, an empirical model, remained unaffected by sample size. Through SHapley Additive exPlanations (SHAP) analysis, elevation, NDVI, slope, land use, and distance to roads and rivers emerged as pivotal indicators for landslide occurrences in the study area, suggesting that human activities significantly influence these events. Five time-varying indicators regarding human activity and climate validated this inference, which provides a new method to identify landslide triggering factors, especially in areas of intense human activity. Based on the findings, a comprehensive framework for LSA is proposed to assist landslide managers in making informed decisions. Future research should focus on expanding model diversity to address the effects of sample size, enhancing the adaptability of the LSA framework, deepening the analysis of human activity impacts on landslides using explainable machine learning techniques, addressing temporal inventory incompleteness in LSA, and critically evaluating model sensitivity to sample size variations across multiple disciplines.