Transparent Reporting Research Articles

Prediction Models for Postoperative Pneumonia in Elderly Hip Fracture Patients: A Systematic Review and Critical Appraisal.

Although several models have been developed to predict postoperative pneumonia in elderly hip fracture patients, no systematic review of the model quality and clinical applicability has been reported. To systematically review and critically appraise existing models for postoperative pneumonia in elderly hip fracture patients. Systematic review and meta-analysis. 10 databases were systematically searched from inception to April 15, 2024, updated on August 26. Two reviewers independently performed literature selection, information extraction and quality assessment. A narrative synthesis was employed to summarise the characteristics of the models. Meta-analysis was performed using Stata 17.0. 13 studies containing 25 models were included. The prevalence of pneumonia was 9.62% (95% CI: 7.62%-11.62%). Age (53.8%), hypoproteinemia (46.2%), chronic obstructive pulmonary disease (COPD, 30.8%), gender (30.8%), activity of daily living score (ADL, 30.8%) and American Society of Anesthesiologists (ASA, 30.8%) score were the top six predictors. All models reported area under curve (AUC: 0.617-0.996). 9 studies (69.2%) used the Hosmer-Lemeshow (H-L) test, calibration curves, or Brier scores to evaluate the calibration. 5 studies (38.5%) performed internal validation, 4 studies (30.8%) performed external validation. All studies had a high risk of bias due to single sample source, inappropriate data processing, inadequate model evaluation, and negligence of calibration and validation. 10 studies (76.9%) had good applicability. Prediction models for postoperative pneumonia in elderly hip fracture patients are still in the developing stage. The validation and evaluation of existing models are poor. Future studies should focus on robust external validation and updating. Additionally, the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + artificial intelligence (TRIPOD+AI) statement should be followed. Prediction models are effective in discriminating postoperative pneumonia in elderly hip fracture patients, but further external validation and adjustment are still warranted.

Effective Solutions for Caregivers of Older Adults: A Review of Systematic Reviews.

This umbrella review aims to describe caregiver interventions tested across populations of informal caregivers of older adults and to examine the effect of caregiver interventions on depression, burden, and quality of life across intervention types and care-recipient populations. PubMed, Scopus, APA PsycInfo, ABIM, Sociological Abstracts, PAIS, and EconLit databases were systematically searched. Forty systematic reviews were identified as meeting inclusion criteria. Most commonly, interventions focused on improving outcomes for caregivers of older adults with dementia. Among the included reviews, caregiver depression was most likely to be reduced by caregiver interventions, and cognitive behavioral therapy (CBT) and mindfulness-based interventions (MBI) were most effective. The use of information and communication technologies to deliver caregiver interventions is increasingly common. Standardization of intervention classifications and transparent reports of intervention delivery details will strengthen research in this field.

Prognostic models for prediction of perioperative allogeneic red blood cell transfusion in adult cardiac surgery: A systematic review and meta-analysis.

Identifying cardiac surgical patients at risk of requiring red blood cell (RBC) transfusion is crucial for optimizing their outcome. We critically appraised prognostic models preoperatively predicting perioperative exposure to RBC transfusion in adult cardiac surgery and summarized model performance. Design: Systematic review and meta-analysis. Studies developing and/or externally validating models preoperatively predicting perioperative RBC transfusion in adult cardiac surgery. Information sources MEDLINE, CENTRAL & CDSR, Embase, Transfusion Evidence Library, Web of Science, Scopus, ClinicalTrials.gov, and WHO ICTRP. Risk of bias and applicability: Quality of reporting was assessed with the Transparent Reporting of studies on prediction models for Individual Prognosis or Diagnosis adherence form, and risk of bias and applicability with the Prediction model Risk of Bias ASsessment Tool. Random-effects meta-analyses of concordance-statistics and total observed:expected ratios for models externally validated ≥5 times. Nine model development, and 27 external validation studies were included. The average TRIPOD adherence score was 66.4% (range 44.1%-85.2%). All studies but 1 were rated high risk of bias. For TRUST and TRACK, the only models externally validated ≥5 times, summary c-statistics were 0.74 (95% CI: 0.65-0.84; 6 contributing studies) and 0.72 (95% CI: 0.68-0.75; 5 contributing studies) respectively, and summary total observed:expected ratios were 0.86 (95% CI: 0.71-1.05; 5 contributing studies) and 0.94 (95% CI: 0.74-1.19; 5 contributing studies), respectively. Considerable heterogeneity was observed in all meta-analyses. Future high quality external validation and model updating studies which strictly adhere to reporting guidelines, are warranted.

Whole slide imaging of tumour microenvironment in classical Hodgkin’s lymphoma: development of a clinical prediction model based on programmed death-ligand 1 and tumorous Reed-Sternberg cells

AimsThe prognostic impact of programmed death-ligand 1 (PD-L1) cells in classic Hodgkin lymphoma (cHL) tumour microenvironment remains undefined.MethodsModel development via Transparent Reporting of a multivariable prediction model for Individual Prognosis...

Risk Prediction Models as an Emerging Trend for Managing Cancer-Related Fatigue: A Systematic Review.

To systematically identify, describe and evaluate the existing risk prediction models for cancer-related fatigue. Systematic review. Seven databases (EMBASE, Cochrane Database, MEDLINE, CINAHL, CNKI, SinoMed and Wanfang) were conducted from inception to August 14, 2023 and updated in September 15, 2024. A systematic search was conducted to identify studies that reported the development of risk prediction models for cancer-related fatigue. Two researchers independently performed a comprehensive assessment of the included studies. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability. Eighteen studies were included in this review. These models predicted cancer-related fatigue in various cancers, including breast cancer, prostate cancer, gynaecological tumours and lung cancer. The most commonly included predictors were anxiety and depression, age, chemotherapy status, sleep quality and pain. Thirteen studies assessed the model performance by using the receiver operating characteristic curve. Although most models exhibited good predictive performance, a higher risk of bias was observed because of inappropriate handling of missing data methods and an imbalance in events per variable. Prediction models show promise for cancer-related fatigue management and precision care, but few are ready for clinical application due to methodological limitations. Future research should focus on improving the clinical utility of cancer-related fatigue models while balancing predictive accuracy with cost-effectiveness to promote equitable care. This study critically systematically evaluated the prediction models of cancer-related fatigue. The existing prediction models have a weak methodological foundation, with only a few having the potential to be implemented in clinical practice. The review is reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis in Systematic Reviews and Meta-Analyses checklist (TRIPOD-SRMA). No patient or public contribution.

Peningkatan Kapasitas Produksi dan Diversifikasi Produk Melalui Mesin Baru dan Inovasi Menu Sehat

This community service program aims to empower members of the Dharma Wanita Persatuan SMPN 1 Rowokangkung through the management of a healthy canteen as part of efforts to improve economic welfare. Although this canteen has great potential, partners face challenges such as limited production capacity, lack of product variety, and inability to prepare financial reports that meet standards. To overcome these problems, this program offers solutions that focus on three main aspects: 1) Increasing production capacity through the procurement of new machines, such as cup sealer machines, freezers, and electric ovens; 2) Product diversification with the innovation of six new healthy and attractive menus for students, which are implemented through training and technology application; and 3) Increasing partners' understanding and skills in preparing financial reports based on Financial Accounting Standards for Micro, Small, and Medium Entities. The implementation method consists of five stages: socialization, training, technology application, mentoring, and evaluation, as well as program sustainability efforts. This program has succeeded in increasing the production capacity of the healthy canteen, where daily production has increased by more than 50% thanks to new machines. Product diversification added six healthy menu variations that were more attractive to consumers, thus increasing the appeal of the canteen. In addition, partner members showed significant improvements in their ability to prepare more transparent and standardized financial reports, with average test scores increasing from 53 to 93 after the training. Overall, this program improves the economy of partner members and contributes to creating a healthier school environment. The program's sustainability is driven by the partners' commitment to continue developing healthy canteens, making it a real effort to empower the economy and welfare of the school community.

AI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review

Abstract STUDY QUESTION How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery? SUMMARY ANSWER AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes and a lack of validation studies restrict the overall generalizability of studies in this area. WHAT IS KNOWN ALREADY Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established. STUDY DESIGN, SIZE, DURATION A comprehensive literature search was conducted following PRISMA-ScR guidelines, covering PubMed and Scopus databases from 2013 to May 15th, 2024. Relevant English-language studies were identified using Medical Subject Headings (MeSH) terms. We also used PubMed’s “similar articles” and “cited by” features for thorough bibliographic screening to ensure comprehensive coverage of relevant literature. PARTICIPANTS/MATERIALS, SETTING, METHODS The review included studies on patients with NOA where AI-based models were used for predicting m-TESE outcomes, by incorporating clinical data, hormonal levels, histopathological evaluations and genetic parameters. Various machine learning and deep learning techniques, including logistic regression, were employed. The Prediction Model Risk of Bias Assessment Tool (PROBAST) evaluated the bias in the studies, and their quality was assessed using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines, ensuring robust reporting standards and methodological rigour. MAIN RESULTS AND THE ROLE OF CHANCE Out of 427 screened articles, 45 met the inclusion criteria, with most using logistic regression and machine learning to predict m-TESE outcomes. AI-based models demonstrated strong potential by integrating clinical, hormonal and biological factors. However, limitations of the studies included small sample sizes, legal barriers and challenges in generalizability and validation. While some studies featured larger, multi-centre designs, many were constrained by sample size. Most studies had a low risk of bias in participant selection and outcome determination, and two-thirds were rated as low risk for predictor assessment, but the analysis methods varied. LIMITATIONS, REASONS FOR CAUTION The limitations of this review include the heterogeneity of the included research, potential publication bias and reliance on only two databases (PUBMED and SCOPUS), which may limit the scope of findings. Additionally, the absence of a meta-analysis prevents quantitative assessment of the consistency of models. Despite this, the review offers valuable insights into AI predictive models for m-TESE in NOA. WIDER IMPLICATIONS OF THE FINDINGS The review highlights the potential of advanced AI techniques in predicting successful sperm retrieval for NOA patients undergoing m-TESE. By integrating clinical, hormonal, histopathological and genetic factors, AI models can enhance decision-making and improve patient outcomes, reducing the number of unsuccessful procedures. However, to further enhance the precision and reliability of AI predictions in reproductive medicine, future studies should address current limitations by incorporating larger sample sizes and conducting prospective validation trials. This continued research and development is crucial for strengthening the applicability of AI models and ensuring broader clinical adoption. STUDY FUNDING/COMPETING INTEREST(S) The authors would like to acknowledge Mashhad University of Medical Sciences, Mashhad, Iran, for financial support (Grant ID : 4020802). The authors declare no competing interests. REGISTRATION NUMBER N/A

Effect of Self-Proning on Respiratory Functions, Pneumonia Severity, and Mortality Risk Among Patients Diagnosed With Community-Acquired Pneumonia: A Nursing-Based Quasi-Experimental Study.

Assessing and monitoring respiratory parameters, such as respiratory rate, oxygen saturation, and lung sounds, is crucial for the management and prognosis of pneumonia patients. Prone positioning has been shown to improve oxygenation in patients with respiratory disorders, including pneumonia, by reducing ventilation/perfusion mismatch. However, there is a lack of evidence supporting the benefits of self-proning in spontaneously breathing pneumonia patients. This study aims to evaluate the effect of self-proning on respiratory functions, pneumonia, and mortality risk among patients diagnosed with community-acquired pneumonia. The study used a pre- and post-test quasi-experimental design with a control group, adhering to the Transparent Reporting of Evaluations with Nonrandomized Designs guidelines. It was conducted in the Medical Respiratory Department inpatient wards at the Respiratory Diseases Hospital in Alexandria, Egypt. The study recruited 128 patients with community-acquired pneumonia, conveniently assigned to an intervention group (n = 64) and a control group (n = 64). Data were collected using socio-demographic and historical data sheets, respiratory parameters assessment sheets, the confusion uremia respiratory rate and blood pressure (CURB-65) severity of pneumonia score, and the Pneumonia Severity Index. Prone positioning significantly positively impacted respiratory parameters in the intervention group compared to the control group. Specifically, the intervention group exhibited improvements in respiratory rate, oxygen saturation, reduced need for supplemental oxygen, and cough (p < .05). Furthermore, the intervention group exhibited fewer changes in findings from chest inspection, palpation, and auscultation. In addition, the severity of pneumonia was reduced in the intervention group compared to the control group, as indicated by lower CURB-65 (p = .014) and pneumonia severity index scores (p = .005). The study demonstrated that self-proning interventions significantly improved respiratory functions and reduced the risk of death among participants with community-acquired pneumonia. These findings suggest that self-proning is a beneficial technique for managing respiratory distress, particularly in non-intubated patients, and can be an effective strategy to improve patient outcomes in clinical settings.

PERANCANGAN SISTEM INFORMASI AKUNTANSI BERBASIS FRAMEWORK CODEIGNITER PADA MASJID AN NUR KABUPATEN SANGGAU

The mosque is a non-profit organization, whose income is based on voluntary donations from the community. The An Nur Mosque's financial management still uses conventional or manual methods. To improve accountable and transparent financial reporting, an accounting information system design was created that uses a website with the codeigniter framework method at the An Nur Mosque in Sanggau Regency, West Kalimantan. This research aims to develop an accounting information system that uses the codeigniter framework, with a focus on making it easier to prepare financial reports for the An Nur Mosque. The research method applied is descriptive qualitative with a waterfall method approach which includes various steps such as needs analysis, design, implementation and testing. The results of this research show that the design of an accounting information system can help non-profit organizations, especially the An Nur Mosque, in preparing more accurate and transparent financial reports.

Is transparency furthering clarity in multilateral climate governance? The case of climate finance

Developing countries’ ambitious climate action hinges on the provision of climate finance from developed countries. Yet, despite decades of debate and contestation over this, substantial controversies continue to persist over whether and how much climate finance is flowing, including whether it is ‘new and additional’ to existing development assistance, whether it targets climate mitigation or adaptation, and whether it is delivered as grants or loans. In this article, we draw on a little-examined source of self-reporting by countries to shed light on these persisting climate finance controversies. Elaborate transparency arrangements have been set up under the United Nations Framework Convention on Climate Change (UNFCCC), whereby countries report on climate finance provided and received. We examine transparency reports submitted by 26 countries from 2014 to 2021 by both developed and developing countries, and international review of these reports, to ascertain the extent to which country self-reporting under the UNFCCC sheds light on the nature of climate finance flows between developed and developing countries. We find that both the reports submitted and the international review of these reports are not furthering clarity around climate finance provided and received. This is because of (a) a persisting lack of multilaterally agreed definitions of key aspects of climate finance provided, and a resulting heterogeneity of definitions used by countries to self-report; and (b) because international review of country self-reporting is subject to politically negotiated limitations. We conclude that clarity remains elusive on climate finance provided and received under the UNFCCC, a situation that seems likely to continue under the Paris Agreement’s enhanced transparency framework, due to be implemented in 2024.

Development and validation of a prediction model for ED using machine learning: according to NHANES 2001–2004

Erectile Dysfunction (ED) is a form of sexual dysfunction in males that imposes significant health and financial burdens globally. Despite its high prevalence, diagnosing ED remains challenging due to the limitations of current diagnostic methods and patients’ reluctance to seek medical help. Currently, some studies have used machine learning techniques for developing ED prediction models, but the performance and interpretability of existing models need to be further improved. This study utilized data from the National Health and Nutrition Examination Survey (NHANES) for the years 2001 to 2004, adhering to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. After excluding male respondents who did not meet the study criteria, a total of 3,869 participants were included. Gradient boosting decision tree (GBDT) algorithms (XGBoost, CatBoost, LightGBM) were used to develop the ED prediction model. Data preprocessing, feature selection, model evaluation, and interpretability analysis were performed to ensure the reliability and effectiveness of the model. The model evaluation results revealed that the AUC values are XGBoost: 0.887 ± 0.016; LightGBM: 0.879 ± 0.016; CatBoost: 0.871 ± 0.019. The F1-Scores are XGBoost: 0.695 ± 0.023; LightGBM: 0.681 ± 0.025; CatBoost: 0.681 ± 0.025. The Recall values are XGBoost: 0.789 ± 0.026; LightGBM: 0.739 ± 0.030; CatBoost: 0.711 ± 0.030. These results confirmed that the XGBoost model is the best-performing ED prediction model in this study. Interpretability analysis results of the XGBoost model showed that age, obesity, cardiovascular risk factors, prostate-related diseases, and socioeconomic status are key features for predicting ED, playing a significant role in the ED mechanism. Therefore, we believe the ED prediction model trained in this study has strong predictive performance and high interpretability. This model can help to expand the diagnostic options for ED, improve the diagnosis rate of ED, and assist doctors in early intervention for patients with ED, ultimately improving patient prognosis.

CEO Connectedness and Firm Transparency

ABSTRACTOur research reveals that CEO connections with Audit Committee directors, established through past employment, education, or social organization memberships, significantly impact firm transparency. These connections increase the likelihood of firms issuing less transparent and readable financial reports. Furthermore, these connections are linked to decreased long‐term firm value and increased crash risk. Our findings underscore the crucial role of CEO connectedness in corporate disclosure transparency and firm value. We employed multiple methodologies to address endogeneity concerns. Our results remain robust.

Do’s and Don’ts in Climate Impact Assessment of University Campuses: Towards Responsible, Transparent and Comprehensive Reporting

With the commitment of more and more universities to decrease greenhouse gas emissions, standardizing the modeling is now becoming urgent. To date, published climate-relevant emissions can be based on completely different and incomparable accounting methods, as shown with results between 6 and 2696 t CO2e for the use phase of the same campus. This article aims to identify, compare, and evaluate the different modeling approaches behind this. For this purpose, this article proposes basic attributes of emissions modeling and reporting. Of the three established approaches to emissions accounting, sector logic (territorial carbon accounting) produces the lowest figures. Reporting in accordance with the greenhouse gas protocol, which has become established worldwide, can also shift the responsibility outside the institutional consumer. Life-cycle assessment, instead, essentially includes provision costs triggered by the consumer. The different modeling approaches also overlap with different coverage of emission sources, for which a standard set is being proposed. Such emissions modeling should finally lead to the determination of university-specific climate performances, i.e., the CO2e emissions per capita and per m2 of gross floor area. Infrastructure and procurement expenses must be recorded in addition and converted to an annual average.

Foundation Model Transparency Reports

Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency. To codify how foundation model developers should provide transparency about the development and deployment of their models, we propose Foundation Model Transparency Reports, drawing upon the transparency reporting practices in social media. While external documentation of societal harms prompted social media transparency reports, our objective is to institutionalize transparency reporting for foundation models while the industry is still nascent. To design our reports, we identify 6 design principles given the successes and shortcomings of social media transparency reporting. To further schematize our reports, we draw upon the 100 transparency indicators from the Foundation Model Transparency Index. Given these indicators, we measure the extent to which they overlap with the transparency requirements included in six prominent government policies (e.g. the EU AI Act, the US Executive Order on Safe, Secure, and Trustworthy AI). Well-designed transparency reports could reduce compliance costs, in part due to overlapping regulatory requirements across different jurisdictions. We encourage foundation model developers to regularly publish transparency reports, building upon recommendations from the G7 and the White House.

Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis

Abstract Aims Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy. Methods and results This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines. PubMed, EMBASE, Web of Science, and Cochrane databases were searched until 1 November 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals (CIs) between ML models and traditional methods in estimating the risk of all-cause mortality, major bleeding, and the composite outcome major adverse cardiovascular events (MACE). Thirty-four models were included across 13 observational studies (4 105 916 patients). For all-cause mortality, the pooled C-statistic for top-performing ML models was 0.89 (95%CI, 0.84–0.91), compared with 0.86 (95% CI, 0.80–0.93) for traditional methods (P = 0.54). For major bleeding, the pooled C-statistic for ML models was 0.80 (95% CI, 0.77–0.84), compared with 0.78 (95% CI, 0.77–0.79) for traditional methods (P = 0.02). For MACE, the C-statistic for ML models was 0.83 (95% CI, 0.75–0.91), compared with 0.71 (95% CI, 0.69–0.74) for traditional methods (P = 0.007). Out of all included models, only one model was externally validated. Calibration was inconsistently reported across all models. Prediction Model Risk of Bias Assessment Tool demonstrated a high risk of bias across all studies. Conclusion Machine learning models marginally outperformed traditional risk scores in the discrimination of MACE and major bleeding following PCI. While integration of ML algorithms into electronic healthcare systems has been hypothesized to improve peri-procedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.

Manajemen Pembiayaan di Sekolah Menengah Pertama (SMP) Negeri 1 Lhokseumawe

The purpose of financial management is to obtain and seek opportunities for funding sources for school activities, in order to use funds effectively and not violate the rules, and create transparent and accountable financial reports. The focus of this study is: 1). Planning, 2). Implementation, 3). Evaluation. This study uses a descriptive qualitative approach with a type of field research using data collection techniques: 1). Observation, 2). Interviews, 3). Documentation. The researcher obtained conclusions from this study, namely: 1). Financial Management Planning at SMP Negeri 1 Lhokseumawe was carried out with a general meeting attended by all principals, treasurers, and education personnel at the school to discuss future planning, then the principal or teacher held a member meeting again which was carried out as a follow-up to the general meeting held. Implementation schedule, time of implementation, place of implementation, and source of funds. 2). Implementation of Financial Management at SMP Negeri 1 Lhokseumawe according to information from the principal, there have never been any violations, so that everything can run smoothly according to the plan that has been made. In this case, the role of the coordinator of each standard is very important, because they are the ones who know best what each standard requires and what activities must be carried out to achieve the management of educational financing in schools. 3). Evaluation of Financing Management at SMP Negeri 1 Lhokseumawe ensures an effective and efficient way of working that leads the organization to the use of educational resources (human or labor, facilities and infrastructure, costs) efficiently and economically. It aims to find what is right and what is wrong, and to use the results of the evaluation to improve performance in the future.

Recommended Minimum Elements for Transparent Reporting of Multi-Jurisdiction Algorithm Feasibility Studies

BackgroundResearch and surveillance using routinely collected health data rely on algorithms to ascertain disease cases or health measures. Where algorithm validation studies are not possible due to lack of a reference standard, algorithm feasibility studies can be used to create and assess algorithms for use in more than one population or jurisdiction. Publication of the methods used to conduct feasibility studies is critical for transparency and reproducibility. Existing guidelines applicable to feasibility studies, including the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) and REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statements, may benefit from additional elements to capture aspects particular to multi-jurisdiction algorithm feasibility studies and ensure full reproducibility. MethodsA subcommittee of members from Health Data Research Network (HDRN) Canada’s Algorithms and Harmonized Data Working Group (AHD-WG) reviewed items within the STROBE and RECORD guidelines and compared these to published feasibility studies. Items not contained within STROBE or RECORD but recommended to ensure transparent reporting of feasibility studies were identified. The AHD-WG reviewed and approved these additional recommended elements. ResultsEleven additional recommended elements were identified: one element for the title and abstract, one in the introduction, five in the methods, and four in the results sections. Elements primarily addressed reporting jurisdictional data variabilities, data harmonization methods, and algorithm implementation. SignificanceImplementation of these recommended elements, alongside the RECORD guidelines, is intended to encourage consistent publication of methods that support reproducibility, as well as increase comparability and international collaborations.

Multivariate time series approaches to extract predictive asthma biomarkers from prospectively patient-collected diary data: a systematic review

ObjectivesLongitudinal data are common in asthma studies, to assess asthma progression in patients and identify predictors of future outcomes, including asthma exacerbations and asthma control. Different methods can quantify temporal...

The Urgency Of Implementing Sharia Accounting In Sharia Financial Institutions In The Indonesian Context: Bibliometric Analysis

Sharia accounting has an important role in a sharia bank to help record financial reports. through recording financial reports will help produce quality, ideal and transparent financial report information. This research aims to determine the mapping of sharia accounting research on Sharia Financial Institutions in Indonesia from 2013 to 2023 and find out the importance of implementing sharia accounting in Sharia Financial Institutions. The approach used is quantitative with bibliometric analysis methods. The data source used is secondary data from the Garuda portal (Garba Reference Digital) and analyzed using VOSviewer software. The research results show that the number of scientific article publications on the application of sharia accounting in sharia financial institutions from 2001 to 2023 was 107 articles divided into 8 clusters and 58 topics.

Using Artificial Intelligence in ESG Assurance

ABSTRACT As environmental, social, and governance (ESG) reporting has become a mainstream channel for companies to communicate their commitment to sustainability issues, the need for reliable and transparent ESG reports is increasing. However, research on ESG assurance is still in its early stages. ESG assurance poses more challenges than traditional financial auditing due to the diverse subjects and types of information in ESG reports. This paper proposes using artificial intelligence (AI) technologies and exogenous data as solutions. It discusses how AI can enhance the efficiency and effectiveness of ESG assurance by assessing vast and extensive data. This paper also explores AI’s application throughout the general ESG assurance process and contributes to the discussion on providing high-quality ESG assurance services. Additionally, it provides practical implications for auditors, regulators, and stakeholders.