Transparent Reporting Research Articles

Patient Safety Issues From Information Overload in Electronic Medical Records.

Background and ObjectiveElectronic health records (EHRs) have become ubiquitous in medicine and continue to grow in informational content. Little has been documented regarding patient safety from the resultant information overload. The objective of this literature review is to better understand how information overload in EHR affects patient safety.MethodsA literature search was performed using the Transparent Reporting of Systematic Reviews and Meta-Analyses standards for literature review. PubMed and Web of Science were searched and articles selected that were relevant to EHR information overload based on keywords.ResultsThe literature search yielded 28 articles meeting the criteria for the study. Information overload was found to increase physician cognitive load and error rates in clinical simulations. Overabundance of clinically irrelevant information, poor data display, and excessive alerting were consistently identified as issues that may lead to information overload.ConclusionsInformation overload in EHRs may result in higher error rates and negatively impact patient safety. Further studies are necessary to define the role of EHR in adverse patient safety events and to determine methods to mitigate these errors. Changes focused on the usability of EHR should be considered with the end user (physician) in mind. Federal agencies have a role to play in encouraging faster adoption of improved EHR interfaces.

MDAR (Material, Design, Analysis Reporting): A Modular Framework for Transparent Reporting in the Life Sciences

MDAR (Material, Design, Analysis Reporting): A Modular Framework for Transparent Reporting in the Life Sciences

Empirical Research and Recommendations for Moral Action: A Plea for the Transparent Reporting of Bridge Principles in Public Health Research

Abstract Academic publications of empirical public health research often entail recommendations for moral action that address practitioners and policy makers. These recommendations are regularly based on implicit moral judgments with the underlying reasons not explicitly stated. In this paper, we elaborate on the moral relevance of such judgments and the need to explain them in order to account for academic argumentation. We argue for an explicit reporting of bridge principles to increase the transparency of the reporting of public health research. The reporting of bridge principles can inform readers, support them in understanding the relationship between empirical and normative claims in a specific paper, and may pave new ways for the rigorous reporting of empirical research that has moral implications. Furthermore, it can be used to classify studies to systematically address the justification for their argumentation.

Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment.

Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors. Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance. Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients. Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool. The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898-0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies. Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity. Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.

A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray.

A COVID-19 diagnosis is primarily determined by RT-PCR or rapid lateral-flow testing, although chest imaging has been shown to detect manifestations of the virus. This article reviews the role of imaging (CT and X-ray), in the diagnosis of COVID-19, focusing on the published studies that have applied artificial intelligence with the purpose of detecting COVID-19 or reaching a differential diagnosis between various respiratory infections. In this study, ArXiv, MedRxiv, PubMed, and Google Scholar were searched for studies using the criteria terms ‘deep learning’, ‘artificial intelligence’, ‘medical imaging’, ‘COVID-19’ and ‘SARS-CoV-2’. The identified studies were assessed using a modified version of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). Twenty studies fulfilled the inclusion criteria for this review. Out of those selected, 11 papers evaluated the use of artificial intelligence (AI) for chest X-ray and 12 for CT. The size of datasets ranged from 239 to 19,250 images, with sensitivities, specificities and AUCs ranging from 0.789–1.00, 0.843–1.00 and 0.850–1.00. While AI demonstrates excellent diagnostic potential, broader application of this method is hindered by the lack of relevant comparators in studies, sufficiently sized datasets, and independent testing.

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

PurposeIn this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities.MethodsThe AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60).ResultsThe model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items).ConclusionThis AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.

Multivariable prediction models for health care spending using machine learning: a protocol of a systematic review

BackgroundWith rising cost pressures on health care systems, machine-learning (ML)-based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending.MethodsWe will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual’s health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g., genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field.DiscussionOur systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending.

Training Business Strategy and Tax on CV. Inara

Strategy is needed in an organization to develop the organization. One strategy that has been developed is related to recording to financial reporting and planning to tax calculation. Transparent financial report will be very useful, especially when it is related to decision making for organizational development. Besides, financial report also becomes the basis in the calculation and reporting of income tax (PPh) of the organization. Training in financial reporting and tax accounting planning will greatly assist the organization, both for decision making and corporate strategic planning. Business strategic training, financial reporting, and taxation are conducted to help CV Inara in achieving better business performance.

XBRL Open Information Model for Risk Based Tax Audit using Machine Learning

Tax audit is an effective instrument for preserving tax compliance, and risk-based tax audit selection can optimize it. Risk-based tax audit selection selectively auditing on high financial risk wealthy taxpayers. In contrast, manually selecting amid the plethora of taxpayer data is difficult, prone to human error, costly and time-consuming. Fortunately, using Extensible Business Report Language (XBRL) as a well-known financial statement reporting standard enables automation. This project proposed software named XAFR as a model for extracting, transforming, and loading the latest XBRL Open Information Model (OIM) 1.0 standard US-SEC dataset and provided it as a data source for risk classification using rule-based risk scoring and Machine Learning. Several thorough testing exposed Random Forest classifier as the best model for Machine Learning risk classification with high accuracy, revealing the excellent collaboration of rule-based risk scoring approach with Machine Learning for risk classification and the importance of XBRL as a transparent but robust report standard that tax authorities can utilize. The excellent system integration resulted in the ability to expose wealthy high-risk taxpayers and high-risk industries and predict risk classification based on two-year financial statements. Moreover, this report introduces the critical importance of RCA (Risk, Current Ratio, Assets) analysis and SIC (Standard Industry Classification) utilization to generate risk classification, rank and explanation. This project utilizes financial indicators in the limited year and leaves the semantic analysis for future works because of time and hardware limitations. The possibility of predicting the possible tax debt prediction are promising Machine Learning future developments

Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.

To evaluate the quality of radiomics studies using cardiac magnetic resonance imaging (CMR) according to the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, and the standards defined by the Image Biomarker Standardization Initiative (IBSI) and identify areas needing improvement. PubMed and Embase were searched to identify radiomics studies using CMR until March 10, 2021. Of the 259 identified articles, 32 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and IBSI standards by two cardiac radiologists. The mean RQS was 14.3% of the maximum (5.16 out of 36). RQS were low for the demonstration of validation (-60.6%), calibration statistics (1.6%), potential clinical utility (3.1%), and open science (3.1%) items. No study conducted a phantom study or cost-effectiveness analysis. The adherence to TRIPOD guidelines was 55.9%. Studies were deficient in reporting title (3.1%), stating objective in abstract and introduction (6.3% and 9.4%), missing data (0%), discrimination/calibration (3.1%), and how to use the prediction model (3.1%). According to the IBSI standards, non-uniformity correction, image interpolation, grey-level discretization, and signal intensity normalization were performed in two (6.3%), four (12.5%), six (18.8%), and twelve (37.5%) studies, respectively. The quality of radiomics studies using CMR is suboptimal. Improvements are needed in the areas of validation, calibration, clinical utility, and open science. Complete reporting of study objectives, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps are necessary. • The quality of science in radiomics studies using CMR is currently inadequate. • RQS were low for validation, calibration, clinical utility, and open science; no study conducted a phantom study or cost-effectiveness analysis. • In stating the study objective, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps, improvements are needed.

Disclosure of Pharmaceutical Industry Funding of Patient Organisations in Nordic Countries: Can Industry Self-Regulation Deliver on its Transparency Promise?

Pharmaceutical companies regularly fund patient organizations. It is important for patient organizations’ credibility that there be transparency regarding this financial support. In Europe, the pharmaceutical industry promises to deliver transparency through self-regulation, as opposed to legally binding provisions, but self-regulation's effectiveness is contested. We compared the industry's transparency of funding in four Nordic countries that, given their general reputation for high transparency, offered a critical test of self-regulation's ability to deliver on its transparency promise. For 2017–2019, we compared: national rules regarding funding disclosure; disclosure practices as evidenced by the availability, accessibility, and format of company transparency reports; and disclosure data, including payment descriptions and sums. Transparency problems differed in kind and magnitude between countries. In Norway and Finland, unlike in Sweden and Denmark, data on funding were difficult to access and analyze and sometimes seemed incomplete or missing. We explain that a key factor allowing for country differences is the freedom given to a country's pharmaceutical industry trade associations to form self-regulatory rules, provided they do not fall below the weak, European-level minimum requirements. Transparency could be improved by aligning rules and practices with the FAIR data principles: that is, corporate disclosures should be findable, accessible, interoperable, and reusable.

Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review

ObjectivesTo summarise multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in paediatrics, describe their performance and completeness in reporting, and determine their potential for application in practice.DesignSystematic review.Data sourceCINAHL,...

An Analysis of Accounting Practices for Zakat, Infaq, and Sadaqah in Lembaga Amil Zakat, Infaq and Sadaqah Nahdatul Ulama (LAZISNU) Parepare, Indonesia: Standardization and Proposed Solutions

The standardization of accounting reporting is important for the purpose of documentation. Such standardization is important, especially when it is related to Islamic instruments, such as zakat, infaq and sadaqah. While certain institutions have already standardized their accounting reporting for zakat, infaq and sadaqah, it is not so apparent when it comes to Lembaga Amil Zakat, Infaq, and Sadaqah Nahdatul Ulama (LAZISNU) Parepare in Indonesia. It seems that LAZISNU is depending on simple bookkeeping based on incomes and expenses made as balance. This research identifies the practice of bookkeeping as carried out by LAZISNU, with the aim to upgrade the practice of their financial statements according to the accounting standard of PSAK 109. A qualitative case study approach was used in this research. The findings showed that the practice of accounting in LAZISNU depends on simple bookkeeping that only shows cash receipts and their disbursements. A recommendation was prepared for financial statements that can be applied by LAZISNU Parepare by preparing a financial position statement (balance sheet), changes in funds, changes in assets under management, cash flow statement, and summary of accounting policies. By improving such financial statements, LAZISNU may disclose proper, accountable and transparent reports to the stakeholders, particularly the muzakki and the government.

Sosialisasi Pengelolaan Dana Desa di Gampong Ujong Drien Kecamatan Meureubo Kabupaten Aceh Barat

The socialization of village fund management in Ujong Drien Village aims to increase the knowledge of village officials about village fund management accounting, so that village officials can make plans, implement programs and activities, which are funded by APBG, and are able to make transparent and accountable village financial reports. This service activity was carried out at the office of the village head Ujong Drien which was attended by 13 participants consisting of the village head, village secretary, and village officials. This service activity runs smoothly according to plan. Meanwhile, the method used in this service activity is to conduct socialization and discussion related to village fund management material. The result of this activity is that the participants know more about village fund management, especially about accounting which leads to transparent and accountable village financial reporting.

Achieving Transparency Report Privacy in Linear Time

An accountable algorithmic transparency report (ATR) should ideally investigate (a) transparency of the underlying algorithm, and (b) fairness of the algorithmic decisions, and at the same time preserve data subjects’ privacy . However, a provably formal study of the impact to data subjects’ privacy caused by the utility of releasing an ATR (that investigates transparency and fairness), has yet to be addressed in the literature. The far-fetched benefit of such a study lies in the methodical characterization of privacy-utility trade-offs for release of ATRs in public, and their consequential application-specific impact on the dimensions of society, politics, and economics. In this paper, we first investigate and demonstrate potential privacy hazards brought on by the deployment of transparency and fairness measures in released ATRs. To preserve data subjects’ privacy, we then propose a linear-time optimal-privacy scheme , built upon standard linear fractional programming (LFP) theory, for announcing ATRs, subject to constraints controlling the tolerance of privacy perturbation on the utility of transparency schemes. Subsequently, we quantify the privacy-utility trade-offs induced by our scheme, and analyze the impact of privacy perturbation on fairness measures in ATRs. To the best of our knowledge, this is the first analytical work that simultaneously addresses trade-offs between the triad of privacy, utility, and fairness, applicable to algorithmic transparency reports.

Endorsement of the TRIPOD statement and the reporting of studies developing contrast-induced nephropathy prediction models for the coronary angiography/percutaneous coronary intervention population: a cross-sectional study

ObjectiveClear and specific reporting of a research paper is essential for its validity and applicability. Some studies have revealed that the reporting of studies based on the clinical prediction models...

Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review

IntroductionType 2 diabetes can lead to gait abnormalities, including a longer stance phase, shorter steps and improper foot pressure distribution. Quantitative data from objective methods for evaluating gait patterns are...

Magnetic Resonance Imaging-Based Radiomics for the Prediction of Progression-Free Survival in Patients with Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis.

Simple SummaryMore than 70% of patients with nasopharyngeal carcinoma (NPC) present with a locoregionally advanced state. Although the initial staging of NPC is primarily based on TNM staging, there is currently no well-established prognostic marker for NPC. Recently, radiomics has received considerable research attention as a potential prognostic biomarker for NPC. The aim of this systematic review and meta-analysis was to comprehensively evaluate the prognostic value of pretreatment magnetic resonance imaging (MRI)-based radiomics for NPC. The analyzed radiomic models demonstrated modest prognostic values, with a pooled mean estimated Harrell’s concordance index (C index) of 0.762. The prognostic models developed using more than eight radiomic features had significantly higher C-indices than those developed using fewer features. Our findings provide evidence that MRI-based radiomics may have a modest prognostic role in the treatment of NPC. However, more consistent study protocols are needed to verify the generalizability of radiomics. Advanced non-metastatic nasopharyngeal carcinoma (NPC) has variable treatment outcomes. However, there are no prognostic biomarkers for identifying high-risk patients with NPC. The aim of this systematic review and meta-analysis was to comprehensively assess the prognostic value of magnetic resonance imaging (MRI)-based radiomics for untreated NPC. The PubMed-Medline and EMBASE databases were searched for relevant articles published up to 12 August 2021. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was used to determine the qualities of the selected studies. Random-effects modeling was used to calculate the pooled estimates of Harrell’s concordance index (C-index) for progression-free survival (PFS). Between-study heterogeneity was evaluated using Higgins’ inconsistency index (I2). Among the studies reported in the 57 articles screened, 10 with 3458 patients were eligible for qualitative and quantitative data syntheses. The mean adherence rate to the TRIPOD checklist was 68.6 ± 7.1%. The pooled estimate of the C-index was 0.762 (95% confidence interval, 0.687–0.837). Substantial between-study heterogeneity was observed (I2 = 89.2%). Overall, MRI-based radiomics shows good prognostic performance in predicting the PFS of patients with untreated NPC. However, more consistent and robust study protocols are necessary to validate the prognostic role of radiomics for NPC.

Reporting Quality of Studies Developing and Validating Melanoma Prediction Models: An Assessment Based on the TRIPOD Statement.

Transparent and accurate reporting is essential to evaluate the validity and applicability of risk prediction models. Our aim was to evaluate the reporting quality of studies developing and validating risk prediction models for melanoma according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) checklist. We included studies that were identified by a recent systematic review and updated the literature search to ensure that our TRIPOD rating included all relevant studies. Six reviewers assessed compliance with all 37 TRIPOD components for each study using the published “TRIPOD Adherence Assessment Form”. We further examined a potential temporal effect of the reporting quality. Altogether 42 studies were assessed including 35 studies reporting the development of a prediction model and seven studies reporting both development and validation. The median adherence to TRIPOD was 57% (range 29% to 78%). Study components that were least likely to be fully reported were related to model specification, title and abstract. Although the reporting quality has slightly increased over the past 35 years, there is still much room for improvement. Adherence to reporting guidelines such as TRIPOD in the publication of study results must be adopted as a matter of course to achieve a sufficient level of reporting quality necessary to foster the use of the prediction models in applications.

Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting.

There has been a considerable increase in total joint arthroplasty (TJA) research using machine learning (ML). Therefore, the purposes of this study were to synthesize the applications and efficacies of ML reported in the TJA literature, and to assess the methodological quality of these studies. PubMed, OVID/MEDLINE, and Cochrane libraries were queried in January 2021 for articles regarding the use of ML in TJA. Study demographics, topic, primary and secondary outcomes, ML model development and testing, and model presentation and validation were recorded. The TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were used to assess the methodological quality. Fifty-five studies were identified: 31 investigated clinical outcomes and resource utilization; 11, activity and motion surveillance; 10, imaging detection; and 3, natural language processing. For studies reporting the area under the receiver operating characteristic curve (AUC), the median AUC (and range) was 0.80 (0.60 to 0.97) among 26 clinical outcome studies, 0.99 (0.83 to 1.00) among 6 imaging-based studies, and 0.88 (0.76 to 0.98) among 3 activity and motion surveillance studies. Twelve studies compared ML to logistic regression, with 9 (75%) reporting that ML was superior. The average number of TRIPOD guidelines met was 11.5 (range: 5 to 18), with 38 (69%) meeting greater than half of the criteria. Presentation and explanation of the full model for individual predictions and assessments of model calibration were poorly reported (<30%). The performance of ML models was good to excellent when applied to a wide variety of clinically relevant outcomes in TJA. However, reporting of certain key methodological and model presentation criteria was inadequate. Despite the recent surge in TJA literature utilizing ML, the lack of consistent adherence to reporting guidelines needs to be addressed to bridge the gap between model development and clinical implementation.