Transparent Reporting Research Articles

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

BackgroundMultiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool.ResultsThirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size.ConclusionsMore research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage.

Explainable Machine Learning to Bring Database to the Bedside: Development and Validation of the TROUT (Trauma fRailty OUTcomes) Index, a Point-of-Care Tool to Prognosticate Outcomes After Traumatic Injury Based on Frailty.

Exemplify an explainable machine learning framework to bring database to the bedside; develop and validate a point-of-care frailty assessment tool to prognosticate outcomes after injury. A geriatric trauma frailty index that captures only baseline conditions, is readily-implementable, and validated nationwide remains underexplored. We hypothesized Trauma fRailty OUTcomes (TROUT) Index could prognosticate major adverse outcomes with minimal implementation barriers. We developed TROUT index according to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis guidelines. Using nationwide US admission encounters of patients aged ≥65 years (2016-2017; 10% development, 90% validation cohorts), unsupervised and supervised machine learning algorithms identified baseline conditions that contribute most to adverse outcomes. These conditions were aggregated into TROUT Index scores (0-100) that delineate 3 frailty risk strata. After associative [between frailty risk strata and outcomes, adjusted for age, sex, and injury severity (as effect modifier)] and calibration analysis, we designed a mobile application to facilitate point-of-care implementation. Our study population comprised 1.6 million survey-weighted admission encounters. Fourteen baseline conditions and 1 mechanism of injury constituted the TROUT Index. Among the validation cohort, increasing frailty risk (low=reference group, moderate, high) was associated with stepwise increased adjusted odds of mortality {odds ratio [OR] [95% confidence interval (CI)]: 2.6 [2.4-2.8], 4.3 [4.0-4.7]}, prolonged hospitalization [OR (95% CI)]: 1.4 (1.4-1.5), 1.8 (1.8-1.9)], disposition to a facility [OR (95% CI): 1.49 (1.4-1.5), 1.8 (1.7-1.8)], and mechanical ventilation [OR (95% CI): 2.3 (1.9-2.7), 3.6 (3.0-4.5)]. Calibration analysis found positive correlations between higher TROUT Index scores and all adverse outcomes. We built a mobile application ("TROUT Index") and shared code publicly. The TROUT Index is an interpretable, point-of-care tool to quantify and integrate frailty within clinical decision-making among injured patients. The TROUT Index is not a stand-alone tool to predict outcomes after injury; our tool should be considered in conjunction with injury pattern, clinical management, and within institution-specific workflows. A practical mobile application and publicly available code can facilitate future implementation and external validation studies.

Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal.

The number of studies applying machine learning (ML) to predict acute kidney injury (AKI) has grown steadily over the past decade. We assess and critically appraise the state of the art in ML models for AKI prediction, considering performance, methodological soundness, and applicability. We searched PubMed and ArXiv, extracted data, and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. Forty-six studies from 3166 titles were included. Thirty-eight studies developed a model, five developed and externally validated one, and three studies externally validated one. Flexible ML methods were used more often than deep learning, although the latter was common with temporal variables and text as predictors. Predictive performance showed an area under receiver operating curves ranging from 0.49 to 0.99. Our critical appraisal identified a high risk of bias in 39 studies. Some studies lacked internal validation, whereas external validation and interpretability of results were rarely considered. Fifteen studies focused on AKI prediction in the intensive care setting, and the US-derived Medical Information Mart for Intensive Care (MIMIC) data set was commonly used. Reproducibility was limited as data and code were usually unavailable. Flexible ML methods are popular for the prediction of AKI, although more complex models based on deep learning are emerging. Our critical appraisal identified a high risk of bias in most models: Studies should use calibration measures and external validation more often, improve model interpretability, and share data and code to improve reproducibility.

Development and Validation of the Medical Emergency Team-Risk Prediction Model for Clinical Deterioration in Acute Hospital Patients, at Time of an Emergency Admission.

To develop and validate a prediction model to estimate the risk of Medical Emergency Team (MET) review, within 48 hours of an emergency admission, using information routinely available at the time of hospital admission. Development and validation of a multivariable risk model using prospectively collected data. Transparent Reporting of a multivariable model for Individual Prognosis Or Diagnosis recommendations were followed to develop and report the prediction model. A 560-bed teaching hospital, with a 22-bed ICU and 24-hour Emergency Department in Melbourne, Australia. A total of 45,170 emergency admissions of 30,064 adult patients (≥18 yr), with an inpatient length of stay greater than 24 hours, admitted under acute medical or surgical hospital services between 2015 and 2017. The outcome was MET review within 48 hours of emergency admission. Thirty candidate variables were selected from a routinely collected hospital dataset based on their availability to clinicians at the time of admission. The final model included nine variables: age; comorbid alcohol-related behavioral diagnosis; history of heart failure, chronic obstructive pulmonary disease (COPD), or renal disease; admitted from residential care; Charlson Comorbidity Index score 1 or 2, or 3+; at least one planned and one emergency admission in the last year; and admission diagnosis and one interaction (past history of COPD × admission diagnosis). The discrimination of the model was comparable in the training (C-statistics 0.82; 95% CI, 0.81-0.83) and the validation set (0.81; 0.80-0.83). Calibration was reasonable for training and validation sets. Using only nine predictor variables available to clinicians at the time of admission, the MET-risk model can predict the risk of MET review during the first 48 hours of an emergency admission. Model utility in improving patient outcomes requires further investigation.

Systematic Literature Review: Aplikasi Siskeudes Meningkatkan Kualitas Akuntabilitas Keuangan Desa Dan Kinerja Perangkat Desa

This study aims to provide a systematic literature review on the factors that influence the Siskeudes application to improve the quality of village financial accountability and the performance of village officials. This study uses a qualitative approach to document research with a systematic literature review as a research data analysis tool. The systematic literature review process consists of three stages, namely planning, implementation, and reporting. The initial study found 460 articles, the study used 12 articles based on inclusion and exclusion criteria for further analysis. There is data extraction that can answer research questions. The results of a systematic literature review from journals from 2015 to 2021 stated that the factors influencing the Siskeudes application to improve the quality of village financial accountability and the performance of village officials were the data input process, application use, information technology infrastructure, human resources, budget and financial knowledge. village, village budget preparation, transparency, and financial reports. The main influencing factors are the process of inputting data and using applications where the village government must provide education and training on a regular basis, coordinate with assistants and trainers from the center, provide support and motivation for staff/employees in the learning process, provide adequate facilities and infrastructure. will be used to improve human resources.

North American COVID-19 Myocardial Infarction (NACMI) Risk Score for Prediction of In-Hospital Mortality.

BackgroundIn-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is higher in those with COVID-19 than in those without COVID-19. The factors that predispose to this mortality rate and their relative contribution are poorly understood. This study developed a risk score inclusive of clinical variables to predict in-hospital mortality in patients with COVID-19 and STEMI.MethodsBaseline demographic, clinical, and procedural data from patients in the North American COVID-19 Myocardial Infarction registry were extracted. Univariable logistic regression was performed using candidate predictor variables, and multivariable logistic regression was performed using backward stepwise selection to identify independent predictors of in-hospital mortality. Independent predictors were assigned a weighted integer, with the sum of the integers yielding the total risk score for each patient.ResultsIn-hospital mortality occurred in 118 of 425 (28%) patients. Eight variables present at the time of STEMI diagnosis (respiratory rate of >35 breaths/min, cardiogenic shock, oxygen saturation of <93%, age of >55 ​years, infiltrates on chest x-ray, kidney disease, diabetes, and dyspnea) were assigned a weighted integer. In-hospital mortality increased exponentially with increasing integer risk score (Cochran-Armitage χ2, P ​< ​.001), and the model demonstrated good discriminative power (c-statistic ​= ​0.81) and calibration (Hosmer-Lemeshow, P ​= ​.40). The increasing risk score was strongly associated with in-hospital mortality (3.6%-60% mortality for low-risk and very high–risk score categories, respectively).ConclusionsThe risk of in-hospital mortality in patients with COVID-19 and STEMI can be accurately predicted and discriminated using readily available clinical information.

Continuing professional education and audit quality: evidence from an emerging market

PurposeIn this paper, the authors examine the association between the amount of continuing professional education (CPE) hours per staff and audit quality in terms of discretionary accruals and audit opinion.Design/methodology/approachSeveral methodologies are adopted to test the hypotheses, including the ordinary least square (OLS) and logistic regression (Logistic). The authors also employ instrument variables regression with two least square (IVREG with 2SLS) and instrument variables probit model (IVProbit) to address the possible endogeneity and strengthen the validity of the main estimation results.FindingsThe main results show that there is a positive and significant relationship between CPE hours per staff and audit quality. As the authors grouped CPE into four areas (finance, auditing and accounting, tax, law and regulations and others) the results are more robust for the sub-sample “accounting and audit” and “others”. Moreover, the findings of this study suggest that CPE hours per staff do not affect audit quality significantly for Big4 audit firms compared to non-Big4 firms.Research limitations/implicationsThe sample size of the present study is quite small because the transparency reports of the audit firms in Turkey have been available since 2013 and the authors could not reach some auditor demographics at the individual level and some attributes at the audit firm level. Besides, some alternative audit quality measures, such as audit effort, audit fees are not employed because they are not disclosed.Originality/valueThis study contributes to the audit literature using Turkish audit firms. The authors believe that the setting of Turkey may yield interesting results because of the data it provides.

Application and Implementation of ZIS Accounting Based on PSAK 109 at Amil Zakat Institutions (Case Study on Laznas BSM Ummah)

This study is based on the background and also aims to determine the existence of zakat and infaq accounting reports based on PSAK 109. This study uses qualitative methods, the approach used in this study is descriptive. The method used is samples of accounting data from 2020 and 2021. The subject of this study is LAZNAS BSM Umat and the object of this study is the application and implementation of ZIS accounting based on PSAK 109 at amil zakat institution (case study at LAZNAS BSM Umat). From the result of this study, the appropriate zakat and infaq/shodaqoh accounting report based on PSAK 109, therefore can be a communication medium between amil institution and other parties, because ZIS financial report is a form of operation responsibility from the activity of collection and distribution of zakat, infaq, and sadaqah (ZIS). To be a transparent and accountable financial report, therefore there should be an accounting standard to organize this. For the application of ZIS accounting in the presentation of financial reports at LAZNAS BSM Umat has implemented ZIS accounting regulations issued by the Indonesian Accounting Association (IAI) 2010.

The ABC-Stroke Score Refines Stroke Risk Stratification in Patients With Atrial Fibrillation at the Emergency Department

AimsTo evaluate the performance of the ABC (Age, Biomarkers, Clinical history) and CHA2DS2-VASc stroke scores under real-world conditions in an emergency setting.Methods and ResultsThe performance of the biomarker-based ABC-stroke score and the clinical variable-based CHA2DS2-VASc score for stroke risk assessment were prospectively evaluated in a consecutive series of 2,108 patients with acute symptomatic atrial fibrillation at a tertiary care emergency department. Performance was assessed according to methods for the development and validation of clinical prediction models by Steyerberg et al. and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis. During a cumulative observation period of 3,686 person-years, the stroke incidence rate was 1.66 per 100 person-years. Overall, the ABC-stroke and CHA2DS2-VASc scores revealed respective c-indices of 0.64 and 0.55 for stroke prediction. Risk-class hazard ratios comparing moderate to low and high to low were 3.51 and 2.56 for the ABC-stroke score and 1.10 and 1.62 for the CHA2DS2-VASc score. The ABC-stroke score also provided improved risk stratification in patients with moderate stroke risk according to the CHA2DS2-VASc score, who lack clear recommendations regarding anticoagulation therapy (HR: 4.35, P = 0.001). Decision curve analysis indicated a superior net clinical benefit of using the ABC-stroke score.ConclusionIn a large, real-world cohort of patients with acute atrial fibrillation in the emergency department, the ABC-stroke score was superior to the guideline-recommended CHA2DS2-VASc score at predicting stroke risk and refined risk stratification of patients labeled moderate risk by the CHA2DS2-VASc score, potentially easing treatment decision-making.

Transparency in the transparency reports: Empirical evidence from Portugal

Purpose: The purpose of this research is to analyze the degree of TR transparency and its relation with the audit firm size, fees, either from audit services, or from other services, and with the human capital.Design/methodology/approach: In this study we considered 282 audit firms, and our final sample includes 268 transparency reports from 2013 through 2017. For this period under analysis, audit firms financial information, human resources information and transparency reports were collected. TR report information was divided in four types of information: Mandatory Information I, Mandatory Information II, Voluntary Information and Extra Information. These indexes measure the fulfillment of each information on the total of information considered in each index. Firstly, a descriptive statistical analysis of the dataset was performed, for them to address the research questions, multivariate analysis was considered.Findings: Our results show a high level of transparency regarding mandatory information I, but a lower accomplishment regarding all other information. In general, the auditor experience, the number of auditors, the number of PIE clients and percentage of extra audit services have a significant impact on the information disclosed in the TR.Originality/value: Our paper contributes to the current literature by assessing the influence that human capital has on the information disclosed in the transparency reports. As far as the authors know, this an original contribution of the paper.

Implementation of the Village Revenue and Expenditure Budget Policy (APBDes) in Infrastructure Development in 2019 (Study in Tiambang Village, Pematang Tiga District, Central Bengkulu Regency)

The purpose of this study is the Implementation of Village Income and Expenditure Budget (APBDes) on Infrastructure Development in 2019 (in Tiambang Village, Pematang Tiga Sub-District, Central Bengkulu Regency). This research is categorized as descriptive qualitative using data collection methods of interviews, observation and documentation. The selection of informants by purposive sampling consisted of seven people using the theory of the principles of APBDes according to Edi Suharto, (2010: 80). Siding with community groups, in making village physical development plans not all village communities are involved and development planning decisions still depend on the village head. Transparent, budget preparation and accountability financial reports for the physical development of village communities do not get accountability reports for development results. can be accounted for, the accountability report and the amount of the village physical development budget is accepted by the community but is not comprehensive. Participation, the community does not participate in the implementation of village physical development because they are only representatives of groups in the community. Democracy, in the physical development process of the village head is always open to receiving suggestions from the village community. Sustainable, Development in the village is budgeted annually in relation to the interests of the village community. Effective, project development runs on time according to the target to be achieved. Efficient, the development carried out by Tiambang village government has been maximized due to visible economic and social changes.

Bias and Reporting Quality of Clinical Prognostic Models for Idiopathic Pulmonary Fibrosis: A Cross-Sectional Study.

ObjectiveThis study aims to evaluate the risk of bias (ROB) and reporting quality of idiopathic pulmonary fibrosis (IPF) prediction models by assessing characteristics of these models.MethodsThe development and/or validation of IPF prognostic models were identified via an electronic search of PubMed, Embase, and Web of Science (from inception to 12 August, 2021). Two researchers independently assessed the risk of bias (ROB) and reporting quality of IPF prediction models based on the Prediction model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariable prognostic model for Individual Prognosis or Diagnosis (TRIPOD) checklist.ResultsTwenty prognostic model studies for IPF were included, including 7 (35%) model development and external validation studies, 8 (40%) development studies, and 5 (25%) external validation studies. According to PROBAST, all studies were appraised with high ROB, because of deficient reporting in the domains of participants (45.0%) and analysis (67.3%), and at least 55% studies were susceptible to 4 of 20 sources of bias. For the reporting quality, none of them completely adhered to the TRIPOD checklist, with the lowest mean reporting score for the methods and results domains (46.6% and 44.7%). For specific items, eight sub-items had a reporting rate ≥80% and adhered to the TRIPOD checklist, and nine sub-items had a very poor reporting rate, less than 30%.ConclusionStudies adhering to PROBAST and TRIPOD checklists are recommended in the future. The reproducibility and transparency can be improved when studies completely adhere to PROBAST and TRIPOD checklists.

DISCRIMINATION, CALIBRATION, AND STRATUM-SPECIFIC LIKELIHOOD RATIOS FOR THE ALDOSTERONOMA RESOLUTION SCORE: A META-ANALYSIS

Objective: The Aldosteronoma Resolution Score (ARS) is the most studied scoring system for predicting the high likelihood of complete resolution of hypertension (CRH) after adrenalectomy for primary aldosteronism (PA). We aimed to perform a meta-analysis of the accuracy, discrimination, and calibration of the ARS using a novel approach to calculation of stratum-specific likelihood ratios (SSLR). Design and method: We searched PubMed, Embase (Ovid), the Cochrane CENTRAL, Web of Science, the Bielefeld Academic Search Engine (BASE) to November 2021 according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. The quality assessment used adapted TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) and PROBAST (Prediction model Risk Of Bias ASsessment Tool) criteria. Results: Thirteen studies comprising 2,158 PA patients from North America (43%), Europe (32%), Asia (22%), and other continents, were included. The pooled estimate of the area under the receiver operating characteristic curve for all studies was 0.77 (95% CI 0.73–0.81), and the ratio of the observed to expected CRH for all studies was 0.9 (95% CI 0.8–1.0). The summary estimates of the SSLR for all studies were 0.3, 0.9, and 3.1, for the low (ARS 0–1), medium (ARS 2–3), and high-likelihood group (ARS 4–5) of CRH, respectively. For studies with a mean follow-up closest to 6, 12, and 36 months, the likelihood ratios were 0.3, 0.8, and 2.6; 0.3, 0.7, and 3.9; and 0.3, 1.8, and 7.0, respectively. When we compared studies that had performed adrenal vein sampling (AVS) in at least 50% of the cohort (mean 77% vs. 38%), the ARS model’s accuracy doubled in the high-likelihood group (ARS 4–5) and tripled in the only study in which all patients were underwent AVS (Figure). Conclusions: In patients with unilateral PA, the ARS is currently the easiest, cheapest, and accurate prediction tool for identifying the long-term high likelihood of CRH after adrenalectomy, particularly when the adrenalectomy is AVS-guided.

A Structured Skin Care Protocol for Preventing and Treating Incontinence-associated Dermatitis in Critically Ill Patients.

To examine the effectiveness of a structured skin care protocol for preventing and treating incontinence-associated dermatitis (IAD) in critically ill patients. Participants were drawn from the ICUs of three teaching hospitals between January 2016 and December 2017. Patients were eligible if they were ≥18 years old, had idiopathic fecal incontinence, had diarrhea but were unable to sense it, and were expected to stay in the ICU for at least 72 hours after developing incontinence. A total of 143 patients were enrolled: 79 in the experimental group and 64 in the control group. In the first phase of the study, routine skin care measures were used; in the second phase, three ICU caregivers were trained to provide a structured skin care protocol. Trained research team members conducted the data collection and analysis. The TREND (Transparent Reporting of Evaluations with Nonrandomized Designs) Statement Checklist was followed in reporting the study results. Application of the structured skin care protocol reduced the incidence of IAD from 35.9% in the control phase to 17.7% in the intervention phase (χ2 = 6.117, P < .05) and also decreased the severity of IAD (z = -2.023, P < .05). Further, IAD developed later (z = -2.116, P < .05) in the intervention group than in the control group. In addition, the nursing times to prevent or manage IAD did not differ significantly between the groups (t = -0.258, P > .05; t = -1.190, P > .05). Use of the developed structured skin care protocol for IAD in critically ill patients lowered the incidence and severity of IAD and delayed IAD development.

Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine

IntroductionWhile there are guidelines for reporting on observational studies (eg, Strengthening the Reporting of Observational Studies in Epidemiology, Reporting of Studies Conducted Using Observational Routinely Collected Health Data Statement), estimation...

Meaningless Applications and Misguided Methodologies in Artificial Intelligence–Related Orthopaedic Research Propagates Hype Over Hope

There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018 and 2021, a total of 178 AI-related articles were published in orthopaedics. However, for every 2 original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular (“AI/machine learning”), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the “black box phenomenon” by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines designed for regression analyses is irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed toward administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable.

Corporate Tax Avoidance and Corporate Social Responsibility Disclosure Readability: Evidence from China

AbstractPaying taxes to support the societies in which they operate is both a legal and ethical responsibility of business. Nevertheless, some companies work to avoid taxes, which could cause society to question the legitimacy of the organisation. Many companies provide reports on their corporate social responsibility (CSR) activities; more transparent CSR reports may help to restore the legitimacy loss associated with tax avoidance. We investigate the relationship between tax avoidance and CSR report readability among Chinese companies. We find a positive relationship between corporate tax avoidance and the readability of CSR reports. This relation is weaker among state‐owned enterprises, which may have stronger pre‐existing legitimacy owing to their relationship with the state. The relationship is also weaker among companies in less developed regions of China, which have less developed institutions to monitor organisational legitimacy. Overall, our results are consistent with the notion that CSR reporting represents an attempt to overcome legitimacy concerns arising from tax avoidance. Our findings indicate that tax avoidance and CSR reporting are alternative means of establishing legitimacy, rather than complementary reflections of an organisational culture that values (or devalues) CSR.

파리협정에 따른 적응 보고의 의의와 우리나라의 참여방안 고찰

Under the Paris Agreement, each Party should submit and update periodically an adaptation communication (ADCOM), independently, as a component of, or in conjunction with other communications or documents, including a national adaptation plan, a nationally determined contribution (NDC), a national communication, or biennial transparency report. Submitted adaptation communications shall be recorded in a public registry. The Glasgow Climate Pact adopted at the 26th Conference of the Parties to the United Nations Framework Convention on Climate Change (COP26) also requested Parties to submit their adaptation communications ahead of the COP27 which will be held in November 2022 so as to provide timely input to the global stocktake. Under this context, however, Korea has not yet submitted its adaptation communication. Surely, the Carbon Neutrality and Green Growth Act for the Climate Change of the Republic of Korea which has just entered into force on 25th March 2022 stipulates that the government can prepare and update an adaptation communication in accordance with the Paris Agreement. Also, the NDC that Korea submitted includes the adaptation component. However the Korean government has not submitted nor recorded it as the adaptation communication. Therefore, the Korean government should make a timely decision on and prepare its submission of the adaptation communication. There has been hardly any studies regarding the preparation, submission, or evaluation of adaptation communications in consideration of Korean context. Hence, this paper discusses the implication of communicating adaptation information and compare possible adaptation reporting instruments that exist under the UNFCCC and the Paris Agreement, for a strategic approach to the preparation of adaptation communication. The paper concludes that it is important to participate in the global efforts for adaptation to climate change and thus recommends for the Korean government to submit and record its adaptation communication in a timely manner. The paper recognizes that both NDC and independent ADCOM are the possible instruments for Korea, but the independent ADCOM will be the best option in case that the government wants to increase the visibility of Korea’s adaptation actions.

CARE guidelines for case reports: explanation and elaboration document. Translation into Russian

BACKGROUND: Well-written and transparent case reports (1) reveal early signals of potential benefits, harms, and information on the use of resources; (2) provide information for clinical research and clinical practice guidelines, and (3) inform medical education. High-quality case reports are more likely when authors follow reporting guidelines. During 20112012, a group of clinicians, researchers, and journal editors developed recommendations for the accurate reporting of information in case reports that resulted in the CARE (CAse REport) Statement and Checklist. They were presented at the 2013 International Congress on Peer Review and Biomedical Publication, have been endorsed by multiple medical journals, and translated into nine languages.&#x0D; OBJECTIVES: This explanation and elaboration document has the objective to increase the use and dissemination of the CARE Checklist in writing and publishing case reports.&#x0D; ARTICLE DESIGN AND SETTING: Each item from the CARE Checklist is explained and accompanied by published examples. The explanations and examples in this document are designed to support the writing of high-quality case reports by authors and their critical appraisal by editors, peer reviewers, and readers.&#x0D; RESULTS AND CONCLUSION: This article and the 2013 CARE Statement and Checklist, available from the CARE website [www.care-statement.org] and the EQUATOR Network [www.equator-network.org], are resources for improving the completeness and transparency of case reports.&#x0D; SOURCE: This article is a translation of the original paper CARE guidelines for case reports: explanation and elaboration document in the Journal of Clinical Epidemiology (doi: 10.1016/j.jclinepi.2017.04.026), prepared under the permission of the copyright holder (Elsevier Inc.), with supervision from the Scientific Editor by Professor E.G. Starostina, MD, PhD (translator) (Moscow, Russia).

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

BackgroundDescribe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.MethodsWe conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models.ResultsSixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available.ConclusionsThe methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.