Criteria Decision Research Articles

Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning

Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.

The Effectiveness of Collaborative Strategic Reading (CSR) in Improving Students’ Reading Comprehension

This study was conducted to investigate the effectiveness of Collaborative Strategic Reading (CSR) in enhancing students’ reading comprehension. Pre-experimental research was applied in the form of one group pre-test/post-test design to understand the influence of CSR on the students’ reading comprehension. Study participants included 30 English as a Foreign Language (EFL) students in their second semester of study in the English Department of a West Java private university. This sampling was determined through purposive sampling. The data for the research question of this study was collected through two test instruments - a pre-test and a post-test - with each consisting of 50 questions in each test in the form of multiple choice and observation. Both tests contained questions based on the five indicators of Barrett's taxonomy: literal comprehension, reorganization, inferential comprehension, evaluation, and appreciation. The study applied a gain-score calculation in which the average N-gain score is 61.38. The results showed that the Sig. (2-tailed) value was 0.000 <0.05. Therefore, based on the decision-making criteria, it can be concluded that there is a significant difference between learning outcomes in the pre-test data and post-test data. This means that the implementation of CSR is quite effective in enhancing the students’ reading comprehension competence.

How debunking biases in research and development decisions could lead to more equitable healthcare?

Decades of research have demonstrated that a variety of cognitive biases can affect our judgment and ability to make rational decisions in personal and professional environments. The lengthy, risky, and costly nature of pharmaceutical research and development (R&D) makes it vulnerable to biased decision-making. Moreover, cognitive biases can play a role in regulatory and clinical decision-making, the latter impacting diagnostic and treatment decisions in the therapeutic use of medicines. These inherent and/or institutionalized biases (e.g., in assumptions, data, or decision-making practices) could conceivably contribute to health inequities. In this mini-review, we provide a broad perspective on how cognitive biases can affect pharmaceutical R&D, regulatory evaluation, and therapeutic decision-making. Example approaches to mitigate the effect of common biases in the development, approval, and use of new therapeutics, such as quantitative decision criteria, multidisciplinary reviews, regulatory and treatment guidelines, and evidence-based clinical decision support systems are illustrated. Mitigating the impact of cognitive biases could increase pharma R&D efficiency, change the perspective and prioritization of unmet medical needs, increase representativeness and quality of evidence generated through clinical trials and real-world research, leading to higher quality insights and more effective medication use, and as such could eventually contribute to more equitable healthcare.

Prototype of a web tool for psycho-pedagogical intervention and stimulation of children with ADHD

Digital tools for students with ADHD have been scarcely implemented in current education due to the challenges they pose. Therefore, rigorous research, from the perspective of web application development, will strengthen students' formative process. In the present study, a web platform prototype was developed to automate the process of psycho-pedagogical intervention in children aged 5 and 12 with ADHD, focused on evaluating how this tool contributes to the process of psycho-pedagogical stimulation. The prototype was developed using XP methodology divided into two general phases and semi-structured interview techniques and structured surveys to gather relevant information. The first general phase involved the construction of the prototype, which included applying the interviews to obtain the system requirements and the BPMN process model of the general pedagogical approach. Interface design, architecture design, and prototype development were also carried out. The second general phase evaluated the system through acceptance and load and stress tests. Finally, usability evaluation was conducted following ISO 25010 and ISO 25040 standards. The usability measure obtained was "good," defined according to decision criteria, allowing us to infer the impact of implementing web tools.

The Influence of Implementing Student Facilitator and Explaining (SFAE) Learning Model on Student Learning Outcomes Assisted by Dakonmatika Learning Media

Students' motivation to study mathematics was essential for improving their learning outcomes in the subject. Integrating the cooperative learning model of Student Facilitator and Explaining (SFAE) with the Dakonmatika learning media represents an innovative blend of group learning and the traditional Indonesian game of Dakon or Congklak. This research aims to determine the effect of using the SFAE learning model assisted by Dakonmatika media on students' mathematics learning outcomes. The study employs a quantitative method with a Quasi-Experimental design in the form of The Nonequivalent Pretest-Posttest Control Group Design. The subjects of this study were 54 students from Muhammadiyah 2 Elementary School in Sorong City, divided into two classes: an experimental class of 29 students and a control class of 25 students. The research findings indicated that, based on the previously described data analysis and discussion of the multiple linear regression test, a significance value of 0.019 < 0.05 was obtained. According to the decision-making criteria, H0 was rejected. Moreover, from the t-test table, the calculated value (2.477) was greater than the table value (2.05553), meaning that t_calculated > t_table. It implied that the application of the SFAE learning model with the assistance of Dakonmatika media has a significant impact on the mathematics learning outcomes of fifth-grade students at Muhammadiyah 2 Elementary School in Sorong City. The implication of this study was that the integration of the Student Facilitator And Explaining (SFAE) learning model with daconmatics media has great potential in improving the quality of mathematics learning in schools.

Health technology assessment for sexual reproductive health and rights benefits package design in sub-Saharan Africa: A scoping review of evidence-informed deliberative processes

Background Health technology assessment uses a multidisciplinary approach to support health benefits package design towards universal health coverage. The evidence-informed deliberative process framework has been used alongside Health technology assessment to enhance stakeholder participation and deliberations in health benefits package design. Applying the evidence-informed deliberative framework for Health assessment could support the morally diverse sexual reproductive health and rights (SRHR) benefits package design process. However, evidence on participation and deliberations for stakeholders in health technology assessment for SRHR benefits package design has not been curated in sub-Saharan Africa. This study synthesises literature to fill this gap. Methods This scoping review applies the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews, and deductive analysis following the evidence-informed deliberative processes framework. The search strategy uses the Guttmacher–Lancet Commission-proposed comprehensive definition of SRHR and the World Health Organisation’s universal health coverage compendium of SRHR interventions to generate search terms. Six databases and biographical hand searches were used to identify studies in Sub-Saharan Africa from 1994. Results A total of 14 studies met the inclusion criteria. Evidence for yearly public budgets and explicit SRHR health technology assessment processes was not found. In 12 of the studies reviewed, new advisory committees were set up specifically for health technology assessment for SRHR priority-setting and benefits package design. In all decision-making processes reviewed, the committee member roles, participation and deliberations processes, and stakeholder veto powers were not clearly defined. Patients, the public, and producers of health technology were often excluded in the health technology assessment for the SRHR benefits package design. Most health technology assessment processes identified at least one decision-making criterion but failed to use this in their selection and appraisal stages for SRHR benefits design. The identification, selection, and scoping stages in health technology assessment for SRHR were non-existent in most studies. In 11 of the 14 processes of the included studies, stakeholders were dissatisfied with the health policy recommendation from the appraisal process in health technology assessment. Perceived benefits for evidence-informed deliberative processes included increased stakeholder engagement and fairness in decision-making. Conclusion To support the integration of diverse social values in health technology assessment for fairer SRHR benefits package design, evidence from this review suggests the need to institutionalise health technology assessment, establish prioritisation decision criteria, involve all relevant stakeholders, and standardise the process and assessment methodological approaches.

On the relationship between subjective decision criteria and paranoid ideations.

Following the conjecture made by (Bliznashki and Hristova in Appetite 167:105645, 2021), we test the hypothesis that liberal subjective decision criteria exhibited during a task involving discrimination between random and systematically correlated patterns should be associated with elevated levels of paranoid ideations. Study 1 establishes the proposed association in the presence of several control measures while also demonstrating that the relationship in question is significantly moderated by subjects' working memory spans and tendencies to be overconfident in their judgments. Study 2 provides further evidence that these effects are indeed specific to tasks involving discrimination between random and systematic patterns and that the observed results are not due to some form of (anti) acquiescence bias or other general trends. Certain specifics of the correlation matrices involving cognitive measures significantly related to the paranoia continuum suggest that our results are consistent with the Entropic Brain Hypothesis. Finally, a simulation study employing a Neural Network demonstrates that increased entropy and liberal decision criteria might be connected to each other with said connection being amenable to an interpretation within the Bayesian paradigm.

Pengaruh Penggunaan Metode Discussion Group terhadap Motivasi dan Hasil Belajar Siswa pada Pembelajaran IPS Kelas VIII di MTs Darussalam Kademangan

This research is motivated by the low motivation and learning outcomes of students in Social Studies. This issue arises because the teaching process is heavily dominated by the teacher, resulting in low student motivation and learning outcomes. Therefore, a teaching method is needed that provides students with the opportunity to construct their own knowledge, one of which is the Discussion Group Method. This method is developed to train students in questioning and answering skills. The Discussion Group Method is very effective for involving students in reviewing the material that has been taught. The research approach used is a quantitative approach with an experimental research type. The population in this study consists of eighth grade students at MTs Darussalam Kademangan. The sample used is 52 students, selected using Purposive Sampling technique. Data collection techniques in the study include questionnaires and tests. To measure both variables, validity and reliability tests of the instruments were conducted, along with prerequisite tests using normality and homogeneity tests, and hypothesis testing using the t-test. The results of the study conducted at MTs Darussalam Kademangan from April 25 to 30, 2024, are as follows: (1) There is a significant influence of the Discussion Group Method on learning motivation. This is evidenced by the t-test results with a Sig. (2-tailed) value of 0.001. Based on the decision-making criteria of 0.001 < 0.05, Ho is rejected and Ha is accepted. (2) There is a significant influence of the Discussion Group Method on learning outcomes. This is evidenced by the t-test results with a Sig. (2-tailed) value of 0.000. Based on the decision-making criteria of 0.000 < 0.05, Ho is rejected and Ha is accepted. Keywords: Discussion Group Method, Learning Motivation, Learning Outcomes

Multi-criteria decision-making for the selection of best airport ground access mode with a new fuzzy rough-entropy based method

In order to cope with the inherent complexities and uncertainties of settings such as transportation problems, the adoption of proper decision procedures becomes a pivotal issue. Particularly, multi-criteria group decision-making (MCGDM) uses multiple evaluations of the options based on several criteria to select the optimal choice and/or rank the alternatives. In this paper we are interested in the Entropy-REGIME method for MCGDM and its ability to handle uncertainty in the presence of fuzzy rough numbers (that allow the practitioner to represent and process imprecise data). To this end we propose a hybrid framework of the fuzzy rough-entropy based REGIME method that involves two main components: fuzzy rough-entropy for the evaluation of the objective weights of the decision criteria, and fuzzy rough-REGIME to rank the alternatives based on the weighted criteria. Its motivation is a case study that concerns the selection of the best transport mode to access Istanbul newly constructed airport. We consider four alternatives for ground access options, namely, underground metro, bus rapid transit, light rail transit, and premium bus services. Our findings show that an underground metro system is the optimal mode, followed by light rail transit, bus rapid transit, and premium bus services. Additionally, to demonstrate the superiority and effectiveness of the proposed technique, comparisons are made with existing methods.

GCT-03. MONOGERM, A NOVEL PROOF-OF-PRINCIPLE BAYESIAN PHASE II TRIAL DESIGN OF CARBOPLATIN OR VINBLASTINE MONOTHERAPY INDUCTION PRIOR TO RADIOTHERAPY FOR INTRACRANIAL GERMINOMA

Abstract BACKGROUND Current European standard-of-care for localised intracranial germinoma is multi-agent chemotherapy (carboPEI: carboplatin/etoposide/ifosfamide) followed by definitive radiotherapy, with excellent survival. MonoGerm is a de-escalation, non-inferiority trial aiming to reduce toxicity. Twelve-week carboplatin (PMID:8039122) AUC10 or vinblastine (PMIDs:32642701/34520101) induction will be evaluated to test if as effective as carboPEI from SIOP-CNS-GCT-II. A novel trial design was required to answer this question pragmatically/safely. METHODS Clinical trials in rare diseases recruit slowly, allowing continuous monitoring of efficacy outcomes. Efficacy-transition-pathways (ETP) are innovative visual tools to aid determination of trial design parameters, and an extension of the dose-transition-pathways concept introduced for dose-finding trials (PMID:28733440). RESULTS MonoGerm includes two monotherapies, with each single arm recruiting six cohorts of three patients, with interim assessment after each recruited cohort and final analysis at 18 patients (total n=36). Insufficient tumour volume response (<30%) at 6-week safety MRI results in 12-weeks carboPEI. Primary outcome is radiological complete response (CR) by 12-weeks of induction monotherapy. A beta-binomial conjugate analysis will generate posterior probability distributions, combining observed trial data as realisations from a binomial distribution with a minimally informative Beta (1,1) prior. Decision criteria to allow early stopping at interim analyses and go/no-go decisions at final analysis are based on probabilities from these posterior distributions. ETP visually maps out parameters used to assert decisions after each interim assessment as a pyramid decision tree. For each recruited cohort and every CR outcome, estimates of the true CR rate and probabilities with associated decisions are mapped out. ETP allows clear communication between statisticians, clinicians, and patient-public-involvement (PPI) teams, facilitating informed decisions in an efficient/realistic trial design. CONCLUSION MonoGerm, a novel Bayesian de-escalation trial, funded by Little Princess Trust (https://www.littleprincesses.org.uk/), uses ETP and continuous monitoring with built-in stopping rules to ensure patient safety in this treatment de-escalation trial.

A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

This paper involves the automation of a visual characterisation technique for corrosion in marine vessels, as it appears in the hull preventive coatings of marine vessels and their surfaces. We propose a module that maximizes the utilisation of features learned by a deep convolutional neural network to identify areas of corrosion and segment pixels in regions of inspection interest for corrosion detection. Our segmentation module is based on Eigen tree decomposition and information-based decision criteria in order to produce specific corroded spots—regions of interest. To assess performance and compare it with our method, we utilize several state-of-the-art deep learning architectures.The results indicate that our method achieves higher accuracy and precision while maintaining the significance score across the entire dataset. To the best of our knowledge, this is the first Eigen tree-based module in the literature in the context of trained neural network predictors for classifying corrosion in marine vessel images.

Development of a harmonized and risk-based code system for post-mortem inspection of broilers

In most European countries, findings detected at post-mortem inspection (PMI) of broilers at slaughter are registered using national or regional code systems. The aim of this study was to map and compare existing national broiler PMI code systems in Europe and to suggest a new harmonized and risk-based code set intended to be interpreted in the same way in all countries. Information about the code systems, condemnation criteria, and the proportion of unfit broiler carcasses in 2022, as well as reasons justifying condemnation, were collected from nine European countries through an online survey. The reasons for declaring meat as unfit for human consumption were divided into food safety, meat quality, broiler health, or broiler welfare. The data were analyzed, and a risk-based code set consisting of 13 codes was developed. Ten of these new codes cover 80% of all condemnations listed in the data retrieved from the countries in the study. The remaining three codes are indicators of broiler welfare. The results were presented in the form of spider diagrams, indicating massive differences between the countries. Harmonization of the code list and the associated decision criteria will allow PMI to be conducted in the same way in different abattoirs and countries. This standardization of the PMI of broilers will enable detailed analysis of PMI results for further use in risk-based meat safety assurance systems and the implementation of computer-based vision systems at PMI in the future.

The evaluation of a stepped care approach for early intervention of borderline personality disorder

BackgroundThe current study evaluated the stepped care approach applied in AtR!Sk; a specialized outpatient clinic for adolescents with BPD features that offers a brief psychotherapeutic intervention (Cutting Down Program; CDP) to all patients, followed by a more intensive Dialectical Behavioral Therapy for Adolescents (DBT-A) for those whose symptoms persist.MethodsThe sample consisted of 127 patients recruited from two AtR!Sk clinics. The number of BPD criteria, psychosocial functioning, severity of overall psychopathology, number of days with non-suicidal self-injury (NSSI; past month), and the number of suicide attempts (last 3 months) were assessed at clinic entry (T0), after CDP (T1), and at 1- and 2-year follow-up (T2, T3). Based on the T1 assessment (decision criteria for DBT-A: ≥ 3 BPD criteria & ZAN-BPD ≥ 6), participants were allocated into three groups; CDP only (n = 74), CDP + DBT-A (eligible and accepted; n = 36), CDP no DBT-A (eligible, but declined; n = 17).ResultsCDP only showed significantly fewer BPD criteria (T2: β = 3.42, p < 0.001; T3: β = 1.97, p = 0.008), higher levels of psychosocial functioning (T2: β = -1.23, p < 0.001; T3: β = -1.66, p < 0.001), and lower severity of overall psychopathology (T2: β = 1.47, p < 0.001; T3: β = 1.43, p = 0.002) over two years compared with CDP no DBT-A, while no group differences were found with regard to NSSI and suicide attempts. There were no group differences between CDP + DBT-A and CDP no DBT-A, neither at T2 nor at T3.DiscussionThe findings support the decision criterion for the offer of a more intense therapy after CDP. However, there was no evidence for the efficacy of additional DBT-A, which might be explained by insufficient statistical power in the current analysis.

The Relationship between Corporate Social Responsibility and Financial Performance

With the rise of sustainable development, Environmental, Social, and Governance (ESG) factors have become crucial for evaluating corporate social responsibility. This research therefore examines the relationship between CSR and company financial outcomes using statistical regression analysis. Strong evidence is noted that the relationship of financial outcomes with Corporate Social Responsibility (CSR) activity postulates a positive relationship amid CSR's shareholder accountability and both the comprehensive CSR index and corporate performance. These findings point out that integration of ESG criteria in investment decisions is important because they provide insight that a company is bound to sustain and meet up with its resistance in the long run. Further, research has outpointed the significance of corporate governance directing CSR efforts towards infusing business with transparency and responsibility. Focusing on sustainability and social responsibility enhances the way a company is perceived, attracts investment, and helps to mitigate risks linked with the environment and society. These are the main observations that underline the importance of public awareness and the necessity for organizations to develop future-oriented CSR approaches that would positively feed into a more sensitive corporate environment and sustainable economic development.

Enhancing healthcare supply chain management through artificial intelligence-driven group decision-making with Sugeno–Weber triangular norms in a dual hesitant q-rung orthopair fuzzy context

The healthcare industry faces numerous challenges in managing its supply chain efficiently, where critical decisions must be made promptly to ensure the availability of essential medical resources. This research introduces a novel artificial intelligence (AI) approach, utilizing the “Sugeno–Weber (SW) t-conorms and t-norms” (SWt-CNs&t-Ns) for decision-making in a Dual Hesitant q-Rung Orthopair Fuzzy (DHq-ROF) context. The SWt-CNs&t-Ns are chosen for their adaptability in data unification, serving as prominent operations for union and intersection processes. Developing a set of fundamental operations is imperative to effectively utilize SWt-CNs&t-Ns and hybrid aggregation operators in DHq-ROF settings. Following the introduction of these processes, several aggregating operators have been provided. These operators include DHq-ROF SW weighted averaging, ordered weighted averaging, hybrid averaging, and their geometric counterparts utilizing DHq-ROF data. The SW triangular norm-based approach aggregates group preferences, facilitating a systematic decision-making process. Triangular norms ensure a realistic representation of interrelationships among decision criteria, leading to optimal healthcare supply chain management solutions. Furthermore, the SW triangular norm-based approach aggregates group preferences, enabling a systematic and comprehensive decision-making process. Choosing the best healthcare supply chain management solutions is easier when you use triangular norms because they give a more accurate picture of how the decision criteria affect each other. The effectiveness of the proposed AI framework is demonstrated through a series of experiments and case studies, showcasing its ability to enhance decision accuracy, reduce uncertainty, and improve overall supply chain performance. The research findings underscore the potential of AI-driven solutions to revolutionize healthcare supply chain management, ultimately leading to better resource allocation, cost efficiency, and improved patient care.

Enabling environmental sustainability and circularity assessment in technology development: The Value-Impact Scanner

The development of new technologies by manufacturing companies can enable more environmentally sustainable and circular solutions. However, there is a lack of tools to assess the sustainability potential of these technologies during research and development. This paper proposes a novel framework to structure and make sense of sustainability and circularity in technology development projects, relating to both value and environmental impacts. The framework was developed into a tool following the Design Research Methodology, in collaboration with a manufacturing company. The tool was tested in three case studies in manufacturing companies, indicating its broad usefulness. The technology assessment tool enables (1) a quick screening of technical solutions or concepts, (2) the definition of decision-making criteria, and (3) the detection of potential negative environmental consequences of innovative technology, especially trade-offs related to circular economy. It was shown to help technology developers to make informed decisions about the environmental sustainability potential of their innovation projects, and to identify areas for improvement.

A Bayesian Measure of Model Accuracy.

Ensuring that the proposed probabilistic model accurately represents the problem is a critical step in statistical modeling, as choosing a poorly fitting model can have significant repercussions on the decision-making process. The primary objective of statistical modeling often revolves around predicting new observations, highlighting the importance of assessing the model's accuracy. However, current methods for evaluating predictive ability typically involve model comparison, which may not guarantee a good model selection. This work presents an accuracy measure designed for evaluating a model's predictive capability. This measure, which is straightforward and easy to understand, includes a decision criterion for model rejection. The development of this proposal adopts a Bayesian perspective of inference, elucidating the underlying concepts and outlining the necessary procedures for application. To illustrate its utility, the proposed methodology was applied to real-world data, facilitating an assessment of its practicality in real-world scenarios.

Quantum and AI-based uncertainties for impact-relation map of multidimensional NFT investment decisions

The purpose of this study is to identify the most essential determinants of NFT investments with a novel artificial intelligence based fuzzy decision-making model. First, the expert choices are prioritized with artificial intelligence methodology. Secondly, the criteria for multidimensional NFT investment decisions are weighted with Quantum picture fuzzy rough sets based DEMATEL. The findings denote that market trends and expected quality have the greatest weight. It is understood that customer satisfaction plays the most crucial role to increase the effectiveness of NFT investments.

Coordinating public intentions and predicted trend in regulatory decisions: A method for demarcation of UGBs

Urban growth boundaries (UGBs) are commonly used for governance of urban growth. For demarcation of UGBs, the prediction models are prized for their automation and high efficiency in spatial data processing. However, public intentions, which cannot be predicted by prediction models, are ignored. This ignorance affects the performance of UGBs and urban development. From the perspective of regulatory tools, this article proposes public intentions and predicted trend as the two decisive elements for demarcation of UGBs. Following this thought, the study develops a method for coordination of their conflicts by providing a decision criteria for policy makers based on estimation of the cost to intervene predicted trend. This method is incorporated into a process of demarcation of UGBs and applied in the case of Tianjin, China. Results show that firstly, policy makers of UGBs should consider conflicts between public intentions and predicted trend and coordinate them for land development. Secondly, in the method for coordination, the decision criteria based on intervention costs can be used to make choice between public intentions and predicted trend and to choose suitable strategies for two types of conflicts. Thirdly, combined with the method for coordination, the process of demarcation is improved with multiple perspectives and methods for providing sufficient reference. Lastly in the case of Tianjin, China, it is verified that the decisions of land development and UGBs are made differently from the considerations of individual or public interests. To optimize the UGBs based on the method for coordination, four categories of strategies are matched with those conflict areas, and supporting measures are proposed correspondingly.

Fast-track recovery after surgery for perforated peptic ulcer safely shortens hospital stay: A systematic review and meta-analysis of six randomized controlled trials and 356 patients.

Postoperative management after surgery for perforated peptic ulcer is still burdened by old traditions. All available data for fast-track recovery in this setting are either very unspecific or underpowered. The aim of this study was to evaluate fast-track recovery in this diagnosis-specific context in a larger sample. Electronic data sources were searched. Eligible studies were randomized controlled trials (RCTs) comparing fast-track recovery and traditional management after surgery for perforated peptic ulcer in adults. A systematic review and meta-analysis was performed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines regulated the process. Quality and risk of bias assessments of individual RCTs were performed by means of the Let Evidence Guide Every New Decision criteria and the Cochrane risk-of-bias tool. Primary endpoints were length of hospital stay and risk of complications. Random or fixed effects modeling were applied as indicated. Outcomes were measured by mean difference and risk difference. Six RCTs with a total cohort of 356 patients were included. Results of our meta-analysis showed significantly shortened length of hospital stay (mean difference -3.50days [95% CI -4.51 to -2.49], p≤0.00001), significantly less superficial and deep surgical-site infections (risk differences -0.12 [95% CI -0.20, -0.05], p=0.002 and -0.03 [95% CI -0.09, 0.03], and p=0.032, respectively), and significantly fewer pulmonary complications (risk difference -0.10 [95% CI -0.17, -0.03], p=0.004) in the fast-track group. This systematic review and meta-analysis shows that fast-track recovery after surgery for perforated peptic ulcer significantly shortened hospital stay in the studied cohort without increasing the risk of postoperative complications.