Transparent Reporting Research Articles

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.

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.

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.

Health technology assessment in mental health services

Mental illnesses have a significant impact on the lives of people not only because of their morbidity but also because of their noticeable impact on economic wellbeing. Out-of-pocket expenditure for mental healthcare services is significant in India and may even lead to impoverishment of the families. The present paper states that Health Technology Assessment (HTA) is necessary for mental healthcare primarily because of its rising cost and competing interests in government decisions and prioritization. HTA does a systematic evaluation of the consequences of using health technology. HTA will provide information to decision makers to develop and implement safer, cost-effective, and efficient policies at the individual and government levels. Appropriate guidance regarding the cost-effectiveness of mental health interventions will help to serve the purpose of providing transparent reports in the context of limited budgets.

The Effect of Nursing Counseling on Treatment Compliance: Acute Coronary Syndrome and Diabetes Mellitus.

The comorbidity of acute coronary syndrome and diabetes affects patient prognoses. Therefore, it is important to manage these diseases simultaneously. In this study, the effect of nursing counseling on treatment compliance was investigated in patients who had received surgical treatment for acute coronary syndrome and had been recently diagnosed with diabetes. A quasi-experimental design with pretest-posttest control group was used. The study sample consisted of 60 patients (intervention group = 30, control group = 30). The data were collected using a patient information form, the Patient Learning Needs Scale, and the Scale for Patient Compliance with Type 2 Diabetes Mellitus Treatment. This study was conducted in compliance with the Transparent Reporting of Evaluations with Nonrandomized Designs checklist. The intervention group earned significantly higher posttest scores on the Patient Learning Needs total scale and subscales than the control group. Moreover, intervention group compliance with treatment was higher than that of the control group. Furthermore, although significant improvements were found in the average posttest body mass index, fasting blood glucose, HbA1c, total cholesterol, triglyceride, and low-density lipoprotein cholesterol values of the control group, the between-group differences in these values were not significant. It is important for nurses to provide counseling services that align with the learning needs of their patients. Also, nursing counseling units should be created and staffed by both specialist nurses and nurse trainers working in healthcare institutions.

The Influence of Human Resource Competencies on The Quality of Financial Statements: Lessons from Indonesia

The increasing demand for good governance has encouraged the government to implement public accountability. One form of accountability in governance is to realize the accountability and transparency of government financial management, through the submission of accountability reports in the form of financial statements. The Navy Headquarters Work Unit is also tasked with preparing accountable and transparent financial reports. Empirical facts show that there are still several problems in realizing the quality of the financial statements (LK) of the Headquarters Satker, especially those caused by the competence of human resources (HR). This study aims to determine the effect of Human Resources competence on the quality of the Headquarters Satker financial statements. The population in this study were personnel serving in the Navy Finance Service (Diskual). The research method used is quantitative with a survey design. The results of this study found that: 1) Human Resource Competence has a positive and significant effect on the quality of Financial Statements. The limitations of this study are put forward as a reference for further research to produce better research. Some of these limitations are the uneven diversity/variation of age, length of service and work experience of respondents (across generations) and data collection carried out at a certain time, so that variations in perception are uneven and cannot be seen continuously.

Gluten-Free Diet Adherence Tools for Individuals with Celiac Disease: A Systematic Review and Meta-Analysis of Tools Compared to Laboratory Tests.

This systematic review aimed to find the tool that best predicts celiac individuals' adherence to a gluten-free diet (GFD). The Transparent Reporting of Multivariable Prediction Models for Individual Prognosis or Diagnosis (TRIPOD-SRMA) guideline was used for the construction and collection of data from eight scientific databases (PubMed, EMBASE, LILACS, Web of Science, LIVIVO, SCOPUS, Google Scholar, and Proquest) on 16 November 2023. The inclusion criteria were studies involving individuals with celiac disease (CD) who were over 18 years old and on a GFD for at least six months, using a questionnaire to predict adherence to a GFD, and comparing it with laboratory tests (serological tests, gluten immunogenic peptide-GIP, or biopsy). Review articles, book chapters, and studies without sufficient data were excluded. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) was used for data collection from the selected primary studies, and their risk of bias and quality was assessed using the Prediction Risk of Bias Assessment Tool (PROBAST). The association between the GFD adherence determined by the tool and laboratory test was assessed using the phi contingency coefficient. The studies included in this review used four different tools to evaluate GFD adherence: BIAGI score, Coeliac Dietary Adherence Test (CDAT), self-report questions, and interviews. The comparison method most often used was biopsy (n = 19; 59.3%), followed by serology (n = 14; 43.7%) and gluten immunogenic peptides (GIPs) (n = 4; 12.5%). There were no significant differences between the interview, self-report, and BIAGI tools used to evaluate GFD adherence. These tools were better associated with GFD adherence than the CDAT. Considering their cost, application time, and prediction capacity, the self-report and BIAGI were the preferred tools for evaluating GFD adherence.

Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study.

Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Machine learning models achieved a ROC-AUC of 0⋅71 ± 0⋅01, an AUC-PR of 0⋅26 ± 0⋅02, a Brier score of 0⋅1 ± 0⋅01 and an expected calibration error of 0⋅07 ± 0⋅04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.

Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review

ObjectiveThis study aimed to investigate the current status and performance of machine learning (ML) approaches in providing reproducible treatment response predictions. MethodsThis systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist. We searched PubMed, Cochrane Library, Web of Science, Scopus, and EBSCO databases for cohort studies that derived and/or validated ML models focused on predicting rheumatoid arthritis (RA) treatment response. We extracted data and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. ResultsFrom 210 unduplicated records identified by the literature search, we retained 29 eligible studies. Of these studies, 10 developed a predictive model and reported a mean adherence to the TRIPOD guidelines of 45.6 % (95 % CI: 38.3–52.8 %). The remaining 19 studies not only developed a predictive model but also validated it externally, with a mean adherence of 42.9 % (95 % CI: 39.1–46.6 %). Most of the articles had an unclear risk of bias (41.4 %), followed by a high risk of bias, which was present in 37.9 %. ConclusionsIn recent years, ML methods have been increasingly used to predict treatment response in RA. Our critical appraisal revealed unclear and high risk of bias in most of the identified models, suggesting that researchers can do more to address the risk of bias and increase transparency, including the use of calibration measures and reporting methods for handling missing data. FundingNone.

Prediction models for skin tears in the elderly: A systematic review and meta-analysis

BackgroundThe prevalence of risk prediction models for skin tears in the elderly is growing; however, there is still debate regarding the usefulness and suitability of these models for clinical use and additional study. ObjectiveThe purpose of this work is to perform a systematic review and meta-analysis of published research on skin tear risk prediction models in the elderly. MethodsWe conducted a comprehensive search of various databases, including Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, PubMed, Web of Science, MEDLINE, Scopus, The Cochrane Library, Wanfang Database, China Science and Technology Journal Database (VIP), and China National Knowledge Infrastructure (CNKI), from the beginning until November 27, 2023. Data extraction from the chosen studies encompassed various elements, such as study design, sample size, outcome definition, data source, predictors, model development, and performance. The assessment of bias and applicability was conducted using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was utilized to assess the transparency in reporting the prediction models—a meta-analysis of the most common predictors to assess predictor reliability. In addition, a narrative synthesis was carried out to provide an overview of the qualities, bias risk, and effectiveness of the current models. The reporting procedures of this meta-analysis conformed to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement. ResultsOut of the initially retrieved 1499 studies, this review included eight prediction models from eight selected studies. All the studies employed logistic regression to develop prediction models for skin tears. The prevalence of skin tears in the elderly varied from 3.0% to 33.3%. Senile purpura and a history of previous skin tears were the most commonly utilized predictors. The reported values for the area under the curve (AUC) ranged from 0.765 to 0.854. All the studies exhibited a high risk of bias, primarily due to inadequate reporting in the outcome and analysis domains. Furthermore, serious questions concerning their applicability were highlighted by four studies. ConclusionBased on the PROBAST checklist, the current models for predicting skin tears in the elderly showed a high risk of bias. The development of new prediction models with bigger sample sizes, appropriate study designs, and external validation from multiple sources ought to be the primary focus of future research. Patient or public contributionThere was no patient or public contribution to this systematic review. RegistrationPROSPERO registration number: CRD42023494387.

The Centers for Medicare & Medicaid Services Tackle Nurse Staffing Rule: Fairy Tale Ending or Endless Litigation?

On April 22, 2024, CMS unveiled the eagerly awaited final rule, titled “Medicare and Medicaid Programs: Minimum Staffing Standards for Long‐Term Care Facilities and Medicaid Institutional Payment Transparency Reporting” (Minimum Staffing Rule), which stirred both anticipation and controversy. The author discusses the highlights and key points to consider regarding this legislation.

Development of the DONOR prediction model on the risk of hypertensive complications in oocyte donation pregnancy: study protocol for a multicentre cohort study in the Netherlands

IntroductionOocyte donation (OD) pregnancy is accompanied by a high incidence of hypertensive complications, with serious consequences for mother and child. Optimal care management, involving early recognition, optimisation of suitable treatment...

Corporate Transparency and Environmental Reporting: Trends and Benefits

Corporate Transparency and Environmental Reporting: Trends and Benefits

An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery: A Systematic Review

BACKGROUND AND OBJECTIVES: Significant evidence has indicated that the reporting quality of novel predictive models is poor because of confounding by small data sets, inappropriate statistical analyses, and a lack of validation and reproducibility. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement was developed to increase the generalizability of predictive models. This study evaluated the quality of predictive models reported in neurosurgical literature through their compliance with the TRIPOD guidelines. METHODS: Articles reporting prediction models published in the top 5 neurosurgery journals by SCImago Journal Rank-2 (Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of NeuroInterventional Surgery, and Journal of Neurology, Neurosurgery, and Psychiatry) between January 1st, 2018, and January 1st, 2023, were identified through a PubMed search strategy that combined terms related to machine learning and prediction modeling. These original research articles were analyzed against the TRIPOD criteria. RESULTS: A total of 110 articles were assessed with the TRIPOD checklist. The median compliance was 57.4% (IQR: 50.0%-66.7%). Models using machine learning-based models exhibited lower compliance on average compared with conventional learning-based models (57.1%, 50.0%-66.7% vs 68.1%, 50.2%-68.1%, P = .472). Among the TRIPOD criteria, the lowest compliance was observed in blinding the assessment of predictors and outcomes (n = 7, 12.7% and n = 10, 16.9%, respectively), including an informative title (n = 17, 15.6%) and reporting model performance measures such as confidence intervals (n = 27, 24.8%). Few studies provided sufficient information to allow for the external validation of results (n = 26, 25.7%). CONCLUSION: Published predictive models in neurosurgery commonly fall short of meeting the established guidelines laid out by TRIPOD for optimal development, validation, and reporting. This lack of compliance may represent the minor extent to which these models have been subjected to external validation or adopted into routine clinical practice in neurosurgery.

Understanding Digital Transformation Village Credit Institutions Towards Sustainability Based on Local Genius

This study aims to understand the digitization transformation of village credit institutions towards sustainability based on local genius. Local geniuses are fundamental values for village credit institutions to enrich traditional local citizens. This research was conducted in village credit institutions and has since been transformed into digital. This research employed the combination methodology of Participatory Action Research in developing the application and Nusantara Paradigm as a view and the method using local values in implementing the application. Data were collected through in-depth interviews with stakeholders’ village credit institutions in Bali Province, the heads of village credit institutions, which is a digital transformation. This study realizes that window-dressing is a common practice that can be described using agency theory. Window dressings can be reduced through digital transformations. Sustainability exists as an enrichment of traditional local economics based on local genius with three harmonized relationships with God, humans, and the environment. Local genius not just the values written in regulation or standard operating procedures, but internalization in daily operational village credit institutions. Sustainability existence and growth measure with the comprehensive approach, harmonization with the God in our spiritual values, harmonization with the human through transparent report can be describe with development Humanism Agency Theory on Traditional Social Community Ownership. Harmonization in an environment with practical digital transformation to save our planet. The originality of this research is that it extends agency theory type 4 in accordance with social community ownership of a new type of conflict. The combined method of Participatory Action Research in developing the application and Nusantara Paradigm in implementing a new methodology approach reflecting digital process transformation based on local values.

Análise dos índices de transparência pública dos municípios do estado do Ceará

The objective of this paper was to calculate the transparency index of municipalities in the state of Ceará over the last five years through reports released by the State Court of Auditors (TCE), which evaluates the periodic disclosure of planning instruments, the Fiscal Management Report and Budget Execution Summary Report, the government's accountability and verifies the real-time availability of revenues and expenses. A methodology was used with qualitative procedures, consulting the legislation and reports released by the TCE, and quantitative procedures, through the calculation of indices. As a result, the municipalities of Ceará have a high transparency index of 0,83. However, it is important to highlight that only two municipalities achieved 100% regularity results. As a result, there is a need for improvement in other city halls regarding the disclosure of public information, with the aim of achieving the maximum level of transparency. Faced with these issues, this work makes a social contribution through research data, influencing citizens to analyze transparency reports and seek to pressure governments to expand measures to disseminate public transparency data. Furthermore, it is worth highlighting the importance of deeper studies on the topic, which evaluate, for example, the factors that impact the lack of disclosure of public data.

Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study

BackgroundAutomatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time. MethodsWe included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing–based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation. ResultsThe dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97–1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months). ConclusionsThese findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.