Quality Ranking Research Articles

Ranking Quality of Life Index in Indonesian Provinces: A Multicriteria Approach for Sustainable Regional Development

ABSTRACTThis study aims to assess and rank the quality of life across 34 provinces in Indonesia using Multi‐Criteria Decision Making (MCDM) with the Organization, Rangement Et Synthèse De Données Relationnelles (ORESTE) method. The assessment was conducted based on seven main domains of quality of life: emotional well‐being, health, employment and productive activities, material well‐being, community participation, personal safety, and environmental quality. The results show that DKI Jakarta, Yogyakarta, and Bali ranked the highest with scores of 1748, 2164, and 2195, respectively. In contrast, Papua, West Papua, and West Sulawesi ranked at the bottom with scores of 5375, 4790, and 4573.5, respectively. The ORESTE method also successfully identified situations of incomparability between some provinces, confirming that not all provinces can be directly compared due to their unique characteristics. Sensitivity analysis reveals that changes in criteria weights do not significantly affect the ranking results, indicating that the outcomes are stable and robust. This study makes a significant contribution by providing a foundation for policymakers to formulate more equitable and inclusive development strategies aimed at reducing disparities in quality of life across provinces in Indonesia.

Sexual dysfunction in lymphoma survivors: a scoping review.

Lymphoma and cancer-related treatment can cause many complications, including sexual dysfunction, a common symptom experienced by lymphoma survivors during the disease trajectory and survivorship. A scoping review was performed to determine the current state of knowledge about sexual dysfunction in lymphoma survivors and identify knowledge gaps and implications for future research. PubMed, Cumulative Index to Nursing and Allied Health Literature, and PsycINFO were searched to identify all primary peer-reviewed research articles published in the English language that were pertinent to sexual dysfunction in lymphoma survivors. The framework of Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews Checklist were used to assess and synthesize the available evidence on sexual dysfunction in lymphoma survivors. This review provided supportive evidence that sexual function in lymphoma survivors is a major ongoing area of concern. Thirty eligible articles published from 1979 to 2023 were appraised in this review. Most of the studies (73.33%) used a cross-sectional design. Using the Methodological Index for Non-Randomized Studies, more than half of the publications (53.33%) achieved a C quality ranking. The reviewed studies revealed that sexual dysfunction was a prevalent symptom experienced by lymphoma survivors, with predictors such as age, gender, sex hormones, stage of disease, and types of cancer treatment. Healthcare providers need to consistently perform routine assessment of sexual function and provide adequate treatment options. The reviewed studies document the problem of sexual dysfunction and offer potential areas of improvement for clinical practice. However, in the majority of the studies, longitudinal data were not obtained which could provide valuable insights on sexual functioning in lymphoma survivors. Despite many years of research on sexual function in lymphoma survivors, substantial gaps in knowledge regarding sexual issues in this population remain. Healthcare providers should routinely assess sexual function of survivors and provide treatment options. Psychosexual interventions should be tailored for the needs of cancer survivors. Sexual function education is essential for all oncologists and should be included in the care pathway from diagnosis until survivorship.

How do palliative care clinicians use their emotions during end-of-life encounters?

Abstract Background Palliative care clinicians are regularly exposed to emotionally laden end-of-life care situations. Until now, the use of their emotions during palliative care consultations has remained understudied. We aimed to develop a theory to understand how palliative care clinicians use their emotions. Methods Qualitative data were collected via individual interviews held with different types of palliative care clinicians across the world (countries were selected based on their Quality of Death Index ranking). We used a social constructivist grounded theory approach to analyze the data using a constant comparative method. Results We held interviews with 21 palliative care clinicians (5 nurses, 11 physicians, 2 psychologists, and 3 social workers) from 10 different countries. Fourteen were female. Participants’ average age was 46 and they had 14 or more years of palliative care experience. The mean length of the interviews was 52 minutes. The process of using emotions could be characterized as follows: first, a difficult end-of-life encounter (e.g. ethical dilemmas or conflicts) would trigger a strong emotion in clinicians. Second, clinicians become aware of the specific emotion via its physical or psychological cues. Third, an assessment of the appropriateness of the use of the emotion follows, in which facilitators and barriers (e.g. the level of emotion skills, the view on emotions, or the feeling of safety in the team) interplay, influencing the clinician to act upon a specific emotion or not. Finally, with a specific intent (e.g. to build connection, stimulate patients and relatives to express their feelings, or clarify a point of view) the clinician shares, shows, or expresses the emotion. Conclusions We found that the strong emotions clinicians experience can be used as a valuable resource to provide palliative care. We also identified key steps to aid clinicians reflect on the appropriateness of using certain emotions in patient care. Key messages • Palliative care clinicians’ emotions can be considered as tools to support palliative care provision. • The developed theory shows potential to explore its applicability in other professions that are also regularly confronted with emotionally charged situations.

Data quality assessment of aggregated LCI datasets: A case study on fossil‐based and bio‐based plastic food packaging

AbstractEnvironmental impacts resulting from plastic food packaging, made from both fossil‐based and bio‐based polymers, are increasingly analyzed in life cycle assessment (LCA) studies. However, the literature reveals significant variations in results for the same polymer within the same scope. To enhance the reliability of these assessments, data quality assessment (DQA) plays a relevant role. However, despite most of the LCA studies employing aggregated life cycle inventory (LCI) datasets, in the literature, DQA methods for aggregated processes are not available. To fill this gap, in this paper, a DQA for aggregated LCI datasets is proposed and demonstrated through its application to 101 aggregated LCI datasets, extracted from Ecoinvent and GaBi databases. The DQA method has been developed by adapting and integrating the pedigree matrix and the data quality ranking proposed by the recently published EC Plastic LCA method. The three data quality indicators (DQIs) used are technological, geographical, and time‐related representativeness. The application of this method exhibits an overall positive evaluation of the selected datasets with differences among the three DQIs. Moreover, it highlights the role of metadata structure in adequately supporting a robust DQA. Indeed, in the absence of a common framework that defines, assesses, and provides access to data quality information, transparency must be assured by the operator in the metadata interpretation and related assumptions along the DQA process. Finally, although the proposed DQA method was developed for the plastic sector, its application can be extended to LCI aggregated datasets relevant to other sectors, materials, and products.

Strategic Evaluation of Whistleblower Software Security in Government: ISO/IEC 25010 and AHP Method

To assess the effectiveness of software security measures in government whistleblower systems, we will utilize the ISO/IEC 25010 standard and the Analytic Hierarchy Process (AHP) methodology. Through the integration of various frameworks, our objective is to build a complete evaluation model that effectively identifies and enhances any vulnerabilities in these crucial systems. The strategy we employ combines the qualitative and quantitative evaluation capabilities of ISO/IEC 25010 and AHP, respectively, to offer a comprehensive perspective on software security performance. The results indicate substantial improvements in the security and reliability of whistleblower software, underscoring the effectiveness of our suggested evaluation technique in identifying crucial areas for refinement. Moreover, the utilization of AHP permitted the ranking of security qualities, guaranteeing focused and efficient improvements. Ultimately, the study emphasizes the significance of thorough security assessments for government whistleblower systems and verifies the effectiveness of utilizing ISO/IEC 25010 and AHP as a methodical approach to improve software security. This research enhances the ongoing endeavor to protect confidential data, fostering a more secure and reliable atmosphere for individuals who expose wrongdoing.

Psychoacoustic assessment of automotive fan-systems

In the automotive industry, electrification trends worldwide are inducing an increase of noise sources emergences. This reinforces the acoustic assessment metrics and thresholds set by stakeholders to ensure acoustic comfort of car passengers and passersby. The main acoustic indicator, the overall Sound Pressure Level, is not relevant enough to depict pedestrian annoyance during exposure to fan-system noises. Currently, psychoacoustic assessment of these products is not systematic as the psychoacoustic metrics taken separately are not sufficient for sound quality ranking. In order to address this weakness, studies were performed by Valeo based on fan-system noise jury evaluations. It aims at providing a preference model consisting of an equation which combines several psychoacoustic metrics to assess fan-system noise annoyance; keeping in mind that Loudness -or overall SPL- high levels could mask underlying annoyances biasing its assessment. In a first step, the correlation between Overall SPL, Loudness and aeraulic power is highlighted based on standard benchmark test database. In a second step, since tonal noises affect the perceived annoyance, additional tonal metrics are analyzed versus Loudness, or acoustic classes. Tonality especially is then compared to other tonal criteria, before potential assessment of a preference model equation.

A Proposed Ensemble Approach for Searching Hacking News Semantically

Efficient search of hacking information has been a topic of great discussion in recent years. Many challenges are encountered when searching for this information. In particular, researchers may encounter unfamiliar and potentially challenging terms, ideas, tools, and other items that are unique to hacking. Effective comprehension of synonyms and polysemy is necessary. These reasons serve as the driving force behind our efforts to develop a productive method for semantic hacking information searches. Semantic search, using advanced NLP techniques, has transformed information retrieval by improving search result accuracy and relevance. Unlike traditional lexical methods, neural models like sentence-transformers handle synonyms and polysemy efficiently. However, processing time increases with model size. This paper proposes a novel ensemble semantic search (NESS) approach that aggregates mini or small neural embedding models, leveraging their distinct advantages. Evaluated on a dataset with over 300,000 Hacker News stories, our proposed method significantly enhances ranking quality and retrieval accuracy compared to existing techniques, while requiring half the processing time of the best-performing large model. The findings underscore the trade-offs between model complexity, retrieval accuracy, and processing efficiency, offering insights for optimizing semantic search systems.

Session context data integration to address the cold start problem in e-commerce recommender systems

Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the ‘cold-start’ problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions’ contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.

PATIENT AND FAMILY INPUT TO DETERMINE EXPERIENCES AND RESEARCH INTERESTS IN PEDIATRIC PANCREATITIS: AN INSPPIRE-2 STUDY.

The aim of this study was to determine patient reported burdensome experiences and research interests in children with acute recurrent pancreatitis or chronic pancreatitis and their families. Children with pancreatitis and their families completed a web-based survey. Subject prioritized rankings of symptoms or quality of life issues and topics for future research were assessed. Data are presented as family and children scores. Among 80 participants, 18 were children with pancreatitis and 62 were family members. Top 5 ranked symptoms or quality of life issues were:1) pain, 2) fatigue, 3) missing school, 4) upset stomach, and 5) not knowing when an attack will occur. Top 5 ranked future research topics were:1) how to prevent a pancreatitis attack, 2) how pancreatitis affects other parts of the body, 3) ways to treat or handle pain, 4) what is the cause of pancreatitis, and 5) teach doctors about pancreatitis. This study highlights the importance of patient and family input in caring for children with pancreatitis. The most bothersome symptoms were pain, fatigue and upset stomach. Children with pancreatitis and families would like future research to primarily focus on prevention of pancreatitis attacks, pain therapy, and complications of pancreatitis.

Rank Aggregation Methods and Tools in Genomic Data Analysis

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. These encompass a broad range of approaches, from distributional and heuristic methods to Bayesian and stochastic optimization algorithms. By examining these techniques, we aim to equip researchers with the background knowledge needed to navigate the intricacies of RA in genomics data integration effectively. We review the practical applications to highlight the relevance and impact of RA methods in advancing genomics research. As the field continues to evolve, we identify open problems and suggest future directions to enhance the effectiveness of rank aggregation in genomics, by addressing the challenges related to data heterogeneity, single-cell omics and spatial transcriptomics data, and the development of clear and consistent evaluation methods. In summary, RA stands as a powerful tool in genomics research, which can offer deeper insights and more comprehensive data integration solutions.

Network analysis of contractors’ assessment factors

The effect of assessment management and selecting the appropriate contractor is growing interest in research because of ‎the change in preferences of contractors’ behaviour and the growth of competitiveness. The assessment of contractors is a continuous process that demands ongoing ‎monitoring to maintain high quality ranks. In this study, the factors behaviour of contractors' assessment for a construction engineering company and a method of topological analysis are examined to a) aid in the interpretation of existing relationships among all factors and b) find influencing factors of contractors’ assessment. The method begins with the network construction of factors considering some criteria or standards. We then propose a method of network analysis and describe the results of studies undertaken to apply the correlation network-based approach and discuss the implications of the methodology. As a result of this study, all influencing factors are involved in a category. The most influential factor is the specialized training of staff that can improve the contractors' productivity and competitiveness.

Circumventing construct-irrelevant variance in international assessments using cognitive diagnostic modeling: A curriculum-sensitive measure

International large-scale assessments such as TIMSS administer achievement tests that are based on an analysis of national curricula to compare student achievement across countries. The organizations that coordinate these studies use Rasch or more generalized item response theory (IRT) models in which all test items are assumed to measure a single latent ability. The test responses are then used to estimate this ability, and the ability scores are used to compare countries.A central but yet-to-be-contested assumption of this approach is that the achievement tests measure an unobserved unidimensional continuous variable that is comparable across countries. One threat to this assumption is the fact that countries and even regions or school tracks within countries have different curricula. When seeking to fairly compare countries, it seems legitimate to account for the fact that applicable curricula differ and that some students may not have been taught the full test content yet. When seeking to fairly compare countries, it seems imperative to account for the fact that national curricula differ and that some countries may not have taught the full test content yet. Nevertheless, existing IRT-based rankings ignore such differences.The present study proposes a direct method to deal with differing curricula and create a fair ranking of educational quality between countries. The new method compares countries solely on test content that has already been taught; it uses information on whether students have mastered skills taught in class or not and does not consider contents that have not been taught yet. Mastery is assessed via the deterministic-input, noisy, “and” gate (DINA) model, an interpretable and tractable cognitive diagnostic model. To illustrate the new method, we use data from TIMSS 1995 and compare it to the IRT-based scores published in the international study report. We find a mismatch between the TIMSS test contents and national curricula in all countries. At the same time, we observe a high correlation between the scores based on the new method and the conventional IRT scores. This finding underscores the robustness of the performance measures reported in TIMSS despite existing differences across national curricula.

Ranking Model Averaging: Ranking Based on Model Averaging

Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses, and there is uncertainty on which scoring function is used to model the responses. In this paper, we address the scoring function uncertainty of continuous response ranking problems by proposing a ranking model averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K-fold crossvalidation criterion based on pairwise loss. We provide two main theoretical properties for RMA. First, we prove that the averaging ranking predictions of RMA are asymptotically optimal in achieving the lowest possible ranking risk. Second, we provide a bound on the difference between the empirical RMA weights and theoretical optimal ones, and we show that RMA weights are consistent. Simulation results validate RMA superiority over competing methods in reducing ranking risk. Moreover, when applied to empirical examples—order priority, wine quality, and piston slap noise—RMA shows its effectiveness in building accurate ranking systems. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This research was supported by the Beijing Municipal Natural Science Foundation [Grant 1222002], the National Natural Science Foundation of China [Grants 12071457, 12201018, 12301364, 71925007, 72091212, and 72273120], National Quality Infrastructure [Grant 2022YFF0609903], the Chinese Academy of Sciences Project for Young Scientists in Basic Research [Grant YSBR-008], and the Natural Science Foundation of Anhui Province [Grant 2308085QA09]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://doi.org/10.1287/ijoc.2023.0257 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0257 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for Ranking Model Averaging: Ranking Based on Model Averaging

Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses and there is uncertainty on which scoring function is used to model the responses. In this paper, we address the scoring function uncertainty of continuous response ranking problems by proposing a Ranking Model Averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K-fold cross-validation criterion based on pairwise loss.

Higher education reputation management through increasing the role of public relations

A university's reputation is an important factor in winning the competition and ensuring sustainability. The reputation built by a university must be about its vision and mission. Reputation needs to be managed professionally by an organization namely quality public relations as the spearhead in delivering and communicating information to all university stakeholders. This research aims to formulate how to manage the reputation of higher education by increasing the role of public relations. The research method used is a descriptive analysis method that combines surveys through questionnaires and in-depth interviews with stakeholders as the main informants. It can be concluded that public relations play an important role in managing higher education reputation based on the results of the research. The built reputation must be based on the values adopted because they become the guideline and direction of the college's development goals. A university's reputation is influenced by accreditation, quality of graduates, human resources, leadership, college rankings, facilities, curriculum, services, community service and innovation, publications and collaborations. Negative factors such as violence, bullying, plagiarism and diploma forgery have the potential to reduce reputation. In reputation management, public relation has a role as a liaison to stakeholders in conveying information about the reputation of higher education. Public relations must manage negative factors that can reduce reputation. The results of this study will be a reference for further research.

An enclosure quality ranking framework for terrestrial animals in captivity

There is a burgeoning interest in measuring and improving animal welfare in captive settings. Recent work has emphasized how enclosure design elements directly impact animal welfare (e.g., from health, behavioral, and reproductive standpoints). Yet, there is no published systematic way for practitioners to quantitatively rank enclosure quality. To address this critical need, we developed a flexible enclosure quality ranking framework for terrestrial animals in captivity. Our enclosure framework comprises 11 broadly applicable and measurable components of enclosure design that have relevance to animal welfare: Display, Size, Shelter, Materials, Environment, Climate, Viewshed, Social, People, Other, and Complexity. Each of these components relates to one or more of the “Five Freedoms” and “Opportunities to Thrive”. In addition to developing the enclosure quality ranking framework, we provide an example of how to apply the framework, and offer suggestions on how to conduct empirical analyses with the ranking data derived from our framework. Once applied, our framework can be used to generate measurable outcomes that practitioners can use to make informed decisions, leading to optimal animal welfare.

Enhancing or attenuating? How patronage ties moderate the impact of performance feedback on performance improvement in the public sector

ABSTRACT This research investigates how performance feedback affects public organizations’ performance improvement and, more importantly, how patronage ties moderate the impact. Using China’s official city air quality ranking as the case, our empirical analyses with standard panel data models and the regression discontinuity design find that negative performance feedback leads to subsequent performance improvement and, more crucially and interestingly, that patronage ties enhance, rather than attenuate, this effect. Essentially, this study deepens and extends the performance feedback theory in the public sector by bringing in informal political institutions as boundary conditions, offering more nuanced understandings of organizational responses to performance feedback.

Air Quality Prediction and Ranking Assessment Based on Bootstrap-XGBoost Algorithm and Ordinal Classification Models

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper.

Unlocking inclusive education: A quality assessment of software design in applications for children with autism

Digital technologies are an essential resource for maximizing education opportunities, yet the COVID-19 pandemic has exposed learning inequities, particularly among underrepresented groups such as children with autism. In this study, we have evaluated the quality of a distinctive dataset of multiplatform software applications, encompassing both assistive and mainstream software, first-hand acquired from special education professionals and families of children with autism. Through a heuristic evaluation based on a system indicators, and an in-depth analysis, we aimed to (1) assess the quality and effectiveness of assistive technologies in supporting the education of children with autism; (2) determine the adaptability of mainstream applications to the unique educational needs of children with autism; and (3) explore the features and constraints of applications targeting children with ASD, categorized according to the needs they cover. The resulting quality ranking, organized by cognitive domains, provides insights into the engagement and effectiveness of applications supporting the learning of children with autism. Furthermore, the findings delineating the functionalities and limitations of these applications contribute to the identification of necessary software engineering best practices. These practices align with user-centered design principles and drive the development of accessible software, thereby fostering high-quality inclusive education for children with autism.

P-194 Developing a classification system for euploid embryos: leveraging morphology grades and biopsy day to accurately rank them based on ongoing pregnancy potential

Abstract Study question Can morphology grades and day of trophectoderm (TE) biopsy be utilized to accurately rank euploid blastocysts according to their potential for ongoing pregnancy (OP)? Summary answer The proposed ranking system categorized euploid blastocysts accurately in terms of their ongoing pregnancy potential. What is known already Implementing a ranking for embryo selection within a pool is a valuable optimization strategy. Many studies have documented the importance of inner cell mass (ICM) and trophectoderm (TE) grading, as well as the day (D) at which blastocysts are biopsied on clinical outcomes. However, literature sparks controversy on clinical findings in relation to blastocyst quality. This is primarily because it reports a diversity in blastocyst grade-ranking systems, and none of them accounting for the day of biopsy. Enhancing consistency and comparability across various research studies could be achieved by establishing a standardized approach for qualitatively classifying and ranking euploid blastocysts. Study design, size, duration This retrospective cohort study included 2209 single euploid frozen embryo transfers (SeFET) between 2017 and 2023. Blastocyst (BL) expansion, TE and ICM were assessed before biopsy according to Gardner’s criteria. Only blastocysts graded ≥BL3CC were biopsied on D5, D6 or D7. Classification models (in-house, logistic regression, gradient boost) were assessed for their calibration, i.e., correct prediction of ongoing pregnancy (OP) probability. Participants/materials, setting, methods In-house ranking system categorized euploid transferred blastocysts (BL3-BL6) as ‘Top-quality’ for blastocysts AA/AB/BA on D5; ‘Good-quality’ for blastocysts BB on D5 and AA/AB/BA/BB on D6; ‘Fair-quality’ for AC/BC/CA/CB on D5, BC on D6 and AA/AB/BA/BB on D7; and ‘Poor-quality’ for blastocysts CC on D5, AC/CA/CB/CC on D6 and CA/CB/AC/BC/CC on D7. The in-house classification ranking system was compared to other developed models to evaluate whether performance could be increased. Main results and the role of chance A total of 406 blastocysts were ranked as Top-quality (100% D5), 1323 as Good-quality (51.3% D5 and 48.7% D6), 190 Fair-quality (57.9% D5, 36.3% D6 and 5.8% D7) and 290 as Poor-quality (12.8% D5, 73.8% D6 and 13.4% D7). The ongoing pregnancy rate was significantly higher for blastocysts ranked as Top compared to Good, Fair and Poor-quality (58.9%, 52.2%, 35.8% and 26.6%, respectively). Miscarriage rates were significantly associated with ranking quality (20.9%, 24.3%, 30.6% and 39.4% for Top, Good, Fair and Poor-quality blastocysts, respectively). We further developed two new classification ranking systems using logistic regression model and XGBoost machine learning model. But the new models provided similar AUC and calibration slope (CS) values as the in-house ranking system (AUC [CS]: 0.604 ± 0.0167 [0.962 ± 0.213] and 0.601 ± 0.0177 [0.949 ± 0.219] vs.0.597 ± 0.015 [0.980 ± 0.230] for logistic regression, XGBoost versus in-house model, respectively). Limitations, reasons for caution The present embryo classification ranking system may not be extrapolated to non-PGT-A cycles. Wider implications of the findings Different grade-ranking systems of blastocysts keep being reported in the different publications. Reaching a consensus to rank euploid blastocysts qualitatively, as the hereby proposed classification, may improve consistency and comparability across different research studies. Crucially, the present ranking-system aids embryo selection for transfer from available pool. Trial registration number not applicable