Assessment Methodology Research Articles

Role of Vena Cava Filter in the Prophylaxis and Treatment of Venous Thromboembolism in Injured Adult Patients: A Systematic Review, Meta-Analysis, and Practice Management Guideline from the Eastern Association for the Surgery of Trauma [RETRACTED

Injured patients are at an increased risk of developing deep vein thrombosis (DVT) and pulmonary embolism (PE). Inferior vena cava (IVC) filters have been used in injured patients to prevent venous thromboembolism (VTE), but current evidence-based guidelines are lacking. Questions regarding IVC filter use in injured patients with clearly defined Population(s), Intervention(s), Comparison(s), and appropriately selected Outcomes (PICO) were formulated. The study sought to understand the evidence behind use of ultra short term IVC filters and use of IVC filters in injured patients with and without known VTE who are unable to receive therapeutic anticoagulation and chemoprophylaxis, respectively. A literature search and review was conducted, followed by meta-analysis. The quality of evidence was assessed per Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. There were twenty-one studies that were analyzed. Three studies were randomized controlled trials (RCTs), three were observational studies, and fifteen studies were retrospective studies. In injured patients without known acute VTE who cannot receive chemoprophylaxis, we recommend against placement of an IVC filter due to associated higher rate of mortality, DVT, PE, and length of stay. The quality of evidence was assessed to be low. In injured patients with known DVT who cannot receive chemoprophylaxis we conditionally recommend against placement of an IVC filter. The quality of evidence was assessed to be very low. No recommendations can be made with respect to placement of ultra short term IVC filters based upon available data. IVC filters should not be placed routinely for prophylaxis in injured adult patients without known VTE who cannot receive chemoprophylaxis. The taskforce conditionally recommends against the placement of IVC filter in injured adult patients with known DVT who cannot receive chemoprophylaxis. Guideline; Systematic review/meta-analysis, level IV.

Theoretical semi-quantitative risk assessment methodology for tunnel design and construction processes

Theoretical semi-quantitative risk assessment methodology for tunnel design and construction processes

Q-Rung orthopair fuzzy 2-tuple linguistic WASPAS algorithm for patients’ prioritization based on prioritized Maclaurin symmetric mean aggregation operators

Due to the fuzziness of the medical field, q-rung orthopair fuzzy 2-tuple linguistic (q-RF2L) set is the privileged way to aid medical professionals in conveying their assessments in the patient prioritization problem. The theme of the present study is to put forward a novel approach centered around the merging of prioritized averaging (PA) and the Maclaurin symmetric mean (MSM) operator within q-RF2L context. According to the prioritization of the professionals and the correlation among the defined criteria, we apply both PA and MSM to assess priority degrees and relationships, respectively. Keeping the pluses of the PA and MSM operators in mind, we introduce two aggregation operators (AOs), namely q-RF2L prioritized Maclaurin symmetric mean and q-RF2L prioritized dual Maclaurin symmetric mean operators. Meanwhile, some essential features and remarks of the proposed AOs are discussed at length. Based on the formulated AOs, we extend the weighted aggregated sum product assessment methodology to cope with q-RF2L decision-making problems. Ultimately, to illustrate the practicality and effectiveness of the stated methodology, a real-world example of patients’ prioritization problem is addressed, and an in-depth analysis with prevailing methods is performed.

A Methodology for Susceptibility Assessment of Wave-Induced Seabed Liquefaction in Silt-Dominated Nearshore Environments

Wave-induced seabed liquefaction significantly jeopardizes the stability of marine structures and the safety of human life. Susceptibility assessment is key to enabling spatial predictions and establishing a solid foundation for effective risk analysis and management. However, the current research encounters various challenges, involving an incomplete evaluation system, poor applicability of methods, and insufficient databases. These issues collectively hinder the accuracy of susceptibility assessments, undermining their utility in engineering projects. To address these challenges, a susceptibility assessment method with the safety factor was developed as the key assessment parameter, allowing for a comprehensive susceptibility assessment across the silt-dominated nearshore environment using Empirical Bayesian Kriging (EBK). The safety factor is determined by combining the cyclic stress ratio (CSR) and the cyclic resistance ratio (CRR), which characterize wave loadings and sediment properties in the study area, respectively. This method was applied in the Chengdao region of the Yellow River Estuary, China, a typical silt-dominated nearshore environment where wave-induced liquefaction events have been reported as being responsible for multiple oil platform and pipeline accidents. By collecting the regional wave and seabed sediment data from cores spanning from 1998 to 2017, the safety factors were calculated, and a zonal map depicting the susceptibility assessment of wave-induced seabed liquefaction was created. This study can serve as a valuable reference for the construction and maintenance of marine engineering in liquefaction-prone areas.

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region.

Novel Feature-Based Difficulty Prediction Method for Mathematics Items Using XGBoost-Based SHAP Model

The level of difficulty of mathematical test items is a critical aspect for evaluating test quality and educational outcomes. Accurately predicting item difficulty during test creation is thus significantly important for producing effective test papers. This study used more than ten years of content and score data from China’s Henan Provincial College Entrance Examination in Mathematics as an evaluation criterion for test difficulty, and all data were obtained from the Henan Provincial Department of Education. Based on the framework established by the National Center for Education Statistics (NCES) for test item assessment methodology, this paper proposes a new framework containing eight features considering the uniqueness of mathematics. Next, this paper proposes an XGBoost-based SHAP model for analyzing the difficulty of mathematics tests. By coupling the XGBoost method with the SHAP method, the model not only evaluates the difficulty of mathematics tests but also analyzes the contribution of specific features to item difficulty, thereby increasing transparency and mitigating the “black box” nature of machine learning models. The model has a high prediction accuracy of 0.99 for the training set and 0.806 for the test set. With the model, we found that parameter-level features and reasoning-level features are significant factors influencing the difficulty of subjective items in the exam. In addition, we divided senior secondary mathematics knowledge into nine units based on Chinese curriculum standards and found significant differences in the distribution of the eight features across these different knowledge units, which can help teachers place different emphasis on different units during the teaching process. In summary, our proposed approach significantly improves the accuracy of item difficulty prediction, which is crucial for intelligent educational applications such as knowledge tracking, automatic test item generation, and intelligent paper generation. These results provide tools that are better aligned with and responsive to students’ learning needs, thus effectively informing educational practice.

Optimizing offshore wind export cable routing using GIS-based environmental heat maps

Abstract. In the United States, there are plans to produce up to 30 GW of offshore wind power by the year 2030, resulting in numerous seabed lease areas which are currently going through the leasing or construction and operations phase. A key challenge associated with offshore wind is optimal routing and installation of the subsea power cables, which transmit power from the main offshore wind energy production area to a land-based station, where it connects to the electrical grid. By traversing a vast extent of the seafloor, the installation and operational phases of subsea power cables have the potential to result in a range of environmental impacts, which may negatively affect sensitive biological, physical, human and/or cultural resource receptors. Presented here is a case study from southeastern North Carolina to identify optimal seabed cable routes and coastal landfalls for a recently leased offshore wind farm by using a combination of publicly available data, coupled with standard environmental impact assessment methodologies and geographic information system (GIS)-based heat maps. The study identified a range of high-risk areas, in addition to a number of potential low-risk routes and landfall areas which minimize seabed user conflicts and impacts on environmentally sensitive locations. Although additional high-resolution and site-specific environmental, geological and biological surveys are required to develop a robust cable installation plan, the preliminary steps from this research optimize early-phase marine spatial planning for offshore wind projects and other similar subsea industries.

Responding to the real problem of sustainable resuscitation skills with real assessment. Mixed-methods evaluation of an authentic assessment programme.

The retention of resuscitation skills is a widespread concern, with a rapid decay in competence frequently following training. Meanwhile, training programmes continue to be disconnected with real-world expectations and assessment designs remain in conflict with the evidence for sustainable learning. This study aimed to evaluate a programmatic assessment pedagogy which employed entrustment decision and the principles of authentic and sustainable assessment (SA). We conducted a prospective sequential explanatory mixed methods study to understand and address the sustainable learning challenges faced by final-year undergraduate paramedic students. We introduced a programme of five authentic assessments based on actual resuscitation cases, each integrating contextual elements that featured in these real-life events. The student-tutor consensus assessment (STCA) tool was configured to accommodate an entrustment scale framework. Each test produced dual student led and assessor scores. Students and assessors were surveyed about their experiences with the assessment methodologies and asked to evaluate the programme using the Ottawa Good Assessment Criteria. Eighty-four students participated in five assessments, generating dual assessor-only and student-led results. There was a reported mean score increase of 9% across the five tests and an 18% reduction in borderline or below scores. No statistical significance was observed among the scores from eight assessors across 420 unique tests. The mean student consensus remained above 91% in all 420 tests. Both student and assessor participant groups expressed broad agreement that the Ottawa criteria were well-represented in the design, and they shared their preference for the authentic methodology over traditional approaches. In addition to confirming local sustainability issues, this study has highlighted the validity concerns that exist with conventional resuscitation training designs. We have successfully demonstrated an alternative pedagogy which responds to these concerns, and which embodies the principles of SA, quality in assessment practice, and the real-world expectations of professionals.

Validation and Evaluation of 5 Scoring Systems for Predicting Metastatic Risk in Pheochromocytoma and Paraganglioma.

Currently, 5 scoring systems have been proposed in the literature for predicting metastatic risk in pheochromocytoma and paraganglioma (PPGL): Pheochromocytoma of the Adrenal Gland Scaled Score (PASS), Grading System for Adrenal Pheochromocytoma and Paraganglioma (GAPP), Composite Pheochromocytoma/paraganglioma Prognostic Score (COPPS), Age, Size, Extra-adrenal location, Secretion type (ASES) score, and Size, Genetic, Age, and PASS (SGAP) model. To validate and evaluate these 5 scoring systems, we conducted a retrospective review of cases diagnosed as PPGL at the Department of Pathology, West China Hospital of Sichuan University, between January 2012 and December 2019. A total of 185 PPGL cases were included, comprising 35 cases with metastasis and 150 cases remained metastasis-free for over 8 years after surgery. The criteria of the 5 scoring systems were used for scoring and risk classification. The predictive performance of the 5 scoring systems was validated, compared, and evaluated using concordance index (C-index) and decision curve analysis (DCA). The C-indices for PASS, GAPP, and SGAP were 0.600, 0.547, and 0.547, respectively, indicating low discriminative ability. In contrast, COPPS and ASES had C-indices of 0.740 and 0.706, respectively, indicating better discriminative performance. DCA also showed that the predictive capability of COPPS was superior to that of ASES, with both outperformed PASS, while PASS had better predictive ability than GAPP and SGAP. Our analysis indicated that pathology-based scoring systems cannot accurately predict metastatic risk of PPGL. Establishing a precise prediction system requires integrating clinical, pathologic, and molecular information, using a scientific methodology for predictive factor selection and weight assessment.

Machine learning-based design of target property-oriented fuels using explainable artificial intelligence

This study presents a machine learning (ML) and explainable artificial intelligence (XAI) integrated framework to guide the experimental investigation of engine combustion toward the most promising fuel compositions. Specifically, the experimental study evaluated nine diesel fuels with differing levels of paraffin, cycloparaffins, and aromatics in a heavy-duty engine, providing key data for subsequent model development. Building on this data, we introduce a multidimensional neural network methodology for feature optimization and performance assessment, incorporating 31 distinct features including engine variables, fuel properties, and components. The methodology innovatively integrates tree-based models with shapley additive explanations (SHAP) for detailed feature importance ranking. Features are sequentially added to subsets based on their importance for multilayer perceptron (MLP) model training, allowing for precise regression performance metrics for each subset. The comprehensive assessment of these best subsets revealed robust regression capabilities, with coefficient of determination (R2) values ranging from 0.9918 to 0.9999, and both root mean square error (RMSE) and mean absolute percentage error (MAPE) maintained below 0.4974 and 0.1388, respectively. Through SHAP and partial dependence plots (PDP), it was demonstrated that optimizing diesel fuel compositions by increasing paraffin levels, reducing aromatics, and moderately increasing cycloparaffins can significantly enhance combustion efficiency and reduce emissions.

Assessing Pipe Condition in Water Distribution Networks

The condition assessment of water distribution pipes is of utmost importance for the prioritization of rehabilitation interventions. However, the application of available methodologies for condition assessment by water utilities with limited human, technological and financial resources is becoming increasingly complex. The current paper aims at the development and application of a methodology for the prediction of the physical condition of water distribution pipes without the need for visual inspection. The methodology includes the development and application of three different algorithms (heuristic, linear regression and support vector regression). The methodology is applied to a water distribution network located in the Algarve region, Portugal. The results obtained from each algorithm are compared with a well-known performance indicator, the ratio of useful life, and present significant differences in its overall pipe condition classification. Results have demonstrated the following: the ratio of useful life tends to distribute pipe classification more equally in the three classes (i.e., good, average and unsatisfactory); the heuristic algorithm classifies most pipes as average condition; and the linear regression algorithm classifies with unsatisfactory conditions. The support vector regression algorithm stands out as the main classifier for identifying pipes in good condition when compared to other algorithms.

Which Provinces Will Be the Beneficiaries of Forestry Carbon Sink Trade? A Study on the Carbon Intensity–Carbon Sink Assessment Model in China

Carbon emissions pose a significant challenge to sustainable development, particularly for China, which is the world’s largest emerging economy and is under pressure to achieve carbon neutrality and reduce emissions amid escalating human activities. The variation in economic development levels and carbon sequestration capacities among its provinces poses a significant hurdle. However, previous research has not adequately examined this dual discrepancy from the perspective of spatial heterogeneity, resulting in a lack of differentiated management of forest carbon sinks across diverse regions. Therefore, to mitigate this discrepancy, this study presents an assessment methodology that analyzes over 100 types of natural and plantation forests using forest age and biomass expansion factors. This study presents a model that can significantly support the efforts of both China and the whole world to achieve carbon neutrality through the improved management of forest carbon sinks. This approach facilitates the assessment of carbon offsets required to meet reduction targets, the development of a provincial framework for carbon intensity and sequestration, and the exploration of their potential for trading markets. Analysis is conducted using MATLAB. Key achievements of this study include the following: (1) The collection of a comprehensive carbon stock dataset for 50 natural and 57 plantation forest types in 31 provinces from 2009 to 2018, highlighting the significant role of new forests in carbon sequestration. (2) The development of a provincial carbon status scoring system that categorizes provinces as carbon-negative, carbon-balancing, or carbon-positive based on local forest sink data and carbon credit demand. (3) The formulation of the carbon intensity–carbon sink assessment (CISA) model, which suggests that provinces with middle- to upper-middle-level economies may have a prolonged need for carbon sink credits during their peak carbon phase. Furthermore, the results show that carbon trading may benefit Guangxi and Yunnan, but may also bring opportunities and risks to Hunan and Hubei. To address regional imbalances, this study advocates tailored policies: carbon-negative and carbon-balancing provinces should enhance carbon sink management, while carbon-positive provinces must focus on energy structure transformation to achieve sustainable development goals.

Correction to: Biomass and bioenergy potentials of bioresidues: assessment methodology development and application to the region of Lafões

Correction to: Biomass and bioenergy potentials of bioresidues: assessment methodology development and application to the region of Lafões

Integrated approach to soil salinity assessment using SEM in Sirdarya province, Uzbekistan

The majority of irrigated land in the Sirdarya area in Uzbekistan is susceptible to salinization. Yet, due to the benefits of its geographical and temporal data, satellite imaging has the ability to efficiently monitor territorial resources. The objectives of this paper are (1) to find how strong surface water mineralization used for irrigation correlated with soil salinity, and (2) to introduce a simplified approach for the soil salinity assessment using SEM based on satellite imagery analysis. The main focus of this research is to explore the potential of using SEM methodology for soil salinity assessment in the study area by streamlining spatial image analysis processes. Throughout data collection of 261 surface water samples and 206 soil samples in Sirdaria irrigation district, while satellite images and other official datasets were our secondary material. We used the Inverse Distance Weighting interpolation method to map soil salinity in ArcGIS, achieving an interpolation accuracy of 72% and a Kappa value of 81%. Statistical packages in R software served as analyst tools to validate our findings. Our findings imply that while MODIS satellite imagery has a lesser connection with EC values, however, the EC values in terms of soil salinization in Sirdarya Province can be predicted using several Landsat bands without using remote sensing indices with correlation coefficients of R ≥ 0.8. We proved that the wavelength ranges of 0.64–0.67 μm and 2.11–2.29 μm are acceptable for mapping soil salinity. Nevertheless, continuous research is needed to understand the mechanisms behind this relationship and the potential applications of these findings in other drylands, land management, and environmental monitoring.

Roleplay: A Tool for Engaging English Language Learners

This article examines the advantages of incorporating roleplay into academic environments, with a specific emphasis on its ability to improve communication and promote the acquisition of vocabulary among students. The study explores the design concerns and assessment methodologies used in roleplay exercises in academic settings, emphasising their efficacy in fostering critical thinking, argumentation skills, and a profound comprehension of course material. The study investigates the impact of roleplay activities on learners' readiness for real-life business interactions, acquisition of negotiation skills, and improvement of professional communication in business environments.

Learning approaches of undergraduate medical students at exit and entry of their medical schools.

Objective: To Compare the learning approaches of medical students at the exit and entry of their medical school, to evaluate the influence of gender and to look for factors leading students towards a change in their learning approach, if any, through interviews. Study Design: Concurrent Mixed Method study. Setting: Ameer-ud-deen Medical College, Lahore. Period: March 2021 to March 2022. Methods: Sequential mixed method 100 first year and 100 Final year MBBS students were included in the study for quantitative analysis. ASSIST Questionnaire is used for identifying student’s approach to studying as either deep, surface or strategic and the correlation of gender with preference of study approach. Descriptive statistics wer`e performed. Student’s t-test was used for statistical comparisons. Qualitative data was collected through in depth semi structured interviews of final year students. Thematic analysis was done to interpret the data and triangulation method was used for validation. Results: There is an over-all predominance of deep approach in the both first and final year MBBS students. The number of first-year students employing a strategic approach were significantly in majority as compared to final year students doing the same. No significant difference is seen in the learning approaches of male and female students. Interviews indicated reasons of shifting away from strategic learning approach in the final year medical students. No significant difference was seen in the selection of approach between female and male students of both groups. Conclusion: Study suggests that there is a predominance of deep approach in the medical students both at exit and entry points. This is due to their clinical exposure and environment, assessment methodologies, curriculum planning, seniors and peer pressure and most importantly their immediate teachers. The more we understand these learning styles, the better we can guide our future generations towards deep learning and improve their attitude and professional skills.

Integrated Approach of Life Cycle Assessment and Experimental Design in the Study of a Model Organic Reaction: New Perspectives in Renewable Vanillin-Derived Chemicals.

This work is focused on performing a quantitative assessment of the environmental impacts associated with an organic synthesis reaction, optimized using an experimental design approach. A nucleophilic substitution reaction was selected, employing vanillin as the substrate, a phenolic compound widely used in the food industry and of pharmaceutical interest, considering its antioxidant and antitumoral potential. To carry out the reaction, three different solvents have been chosen, namely acetonitrile (ACN), acetone (Ace), and dimethylformamide (DMF). The syntheses were planned with the aid of a multivariate experimental design to estimate the best reaction conditions, which simultaneously allow a high product yield and a reduced environmental impact as computed by Life Cycle Assessment (LCA) methodology. The experimental results highlighted that the reactions carried out in DMF resulted in higher yields with respect to ACN and Ace; these reactions were also the ones with lower environmental impacts. The multilinear regression models allowed us to identify the optimal experimental conditions able to guarantee the highest reaction yields and lowest environmental impacts for the studied reaction. The identified optimal experimental conditions were also validated by experimentally conducting the reaction in those conditions, which indeed led to the highest yield (i.e., 93%) and the lowest environmental impacts among the performed experiments. This work proposes, for the first time, an integrated approach of DoE and LCA applied to an organic reaction with the aim of considering both conventional metrics, such as reaction yield, and unconventional ones, such as environmental impacts, during its lab-scale optimization.

Update of the World Health Organization's Mental Health Gap Action Programme Guideline for Psychoses (Including Schizophrenia).

The World Health Organization's (WHOs) Mental Health Gap Action Programme (mhGAP) aims to improve healthcare for mental, neurological, and substance use disorders in nonspecialized settings, with a focus on low- and middle-income countries (LMICs). mhGAP includes guidelines for the treatment of psychoses (including schizophrenia), which were recently updated in 2023. The complexity of the WHO guideline update process and the updated recommendations on psychoses are presented. The WHO guideline development process is outlined as well as the evidence appraisal and the translation of the evidence into recommendations following the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. The guideline update process includes a review of the literature, a compilation of systematic reviews, and extracting data related to critical and important outcomes. The updated recommendations and the justifying evidence are discussed. The WHO mhGAP guidelines for psychoses are adapted to LMICs, and consist of 13 recommendations in 2023, whereof 5 were updated, and 1 recommendation was newly developed. Background information on how these recommendations were obtained, and significant changes since the previous guideline update in 2015 are provided. Unlike other guidelines, the WHO must consider various countries, contextual factors, and the WHO Model Lists of Essential Medicines when developing its guidelines. A transformation of the WHO guideline for psychoses into a living guideline would ensure always up-to-date recommendations and facilitate shared decision-making.

Updated European guidelines for clinical management of familial adenomatous polyposis (FAP), MUTYH-associated polyposis (MAP), gastric adenocarcinoma, proximal polyposis of the stomach (GAPPS) and other rare adenomatous polyposis syndromes: a joint EHTG-ESCP revision.

Hereditary adenomatous polyposis syndromes, including familial adenomatous polyposis and other rare adenomatous polyposis syndromes, increase the lifetime risk of colorectal and other cancers. A team of 38 experts convened to update the 2008 European recommendations for the clinical management of patients with adenomatous polyposis syndromes. Additionally, other rare monogenic adenomatous polyposis syndromes were reviewed and added. Eighty-nine clinically relevant questions were answered after a systematic review of the existing literature with grading of the evidence according to Grading of Recommendations, Assessment, Development, and Evaluation methodology. Two levels of consensus were identified: consensus threshold (≥67% of voting guideline committee members voting either 'Strongly agree' or 'Agree' during the Delphi rounds) and high threshold (consensus ≥ 80%). One hundred and forty statements reached a high level of consensus concerning the management of hereditary adenomatous polyposis syndromes. These updated guidelines provide current, comprehensive, and evidence-based practical recommendations for the management of surveillance and treatment of familial adenomatous polyposis patients, encompassing additionally MUTYH-associated polyposis, gastric adenocarcinoma and proximal polyposis of the stomach and other recently identified polyposis syndromes based on pathogenic variants in other genes than APC or MUTYH. Due to the rarity of these diseases, patients should be managed at specialized centres.

Healthcare performance analytics based on the novel PDA methodology for assessment of efficiency and perceived quality outcomes: A machine learning approach

Efficient resource allocation in healthcare organizations remains a complex challenge, exacerbated by the intricate data structure and myriad variables involved. While traditional methods like Data Envelopment Analysis have been employed, these often lack the requisite discriminatory power for accurate evaluation. This study introduces the PDA methodology, a fusion of Principal Component Analysis, Data Envelopment Analysis, and Analysis of Variance algorithms, developed in a machine learning environment. To further optimize discriminative power, the Particle Swarm Optimization Algorithm was included. By applying the PDA methodology to hospitals in the Apulia region of Italy, we found that facilities with better perceived quality tend to be more efficient. Furthermore, we identified significant variations in efficiency based on network affiliation and hospital level. Machine learning algorithms, including linear regressions and neural networks, confirmed that our methodology outperforms traditional Data Envelopment Analysis models in precisely distinguishing between varying levels of hospital efficiency. In conclusion, this work provides a fresh perspective for future research and policy decisions in the healthcare domain, demonstrating how the use of advanced and machine learning algorithms can result in more accurate evaluations and targeted interventions.