Transparent Reporting Research Articles

Criteria for selection to anesthesia residency programs: a survey of Canadian anesthesia program directors

IntroductionApplicants to specialty programs lack guidance on knowing what exactly is desired by selection committees and program directors. Anesthesia is especially opaque, given its failure to provide transparency reports nationally. This study was developed to survey Canadian anesthesia program directors about the aspects of the application package desired in an anesthesia applicant. The primary objective is to identify the preferred attributes of anesthesia applications by those mandating the selection committees.MethodsSurvey was developed via Google Surveys, and sent online over a period of two months in June and July 2020. All program directors were sent requests for filling in the survey. STATA was used for all statistical analyses. Two analyses, Mann-Whitney and ANOVA tests, were performed for comparison groups. A p < 0.05 was considered significant.ResultsFourteen of seventeen (83%) Canadian anesthesia program directors completed the survey. Having done an anesthesia elective, good performance in it, and excellence of preclinical academic performance were considered among the most important aspects of the application package with the highest ranking important and smallest standard deviation. Any form of red flag was also considered an important criterion, again with little variation among program directors. The reference letters selected by the applicants were also important, with a personal relationship and well written reference being identified as most important (p < 0.05).ConclusionsAn applicant who has good academic performance, having anesthesia elective experience, personal, well-written reference letters, and general activity and interests that are not necessarily anesthesia-focused would be favoured by Canadian anesthesia programs.

Effectiveness of a musical fitness programme for older adults with cognitive impairment in long-term care facilities: A quasi-experimental study.

To investigate the effectiveness of a musical fitness programme (MFP) intervention in improving neuropsychiatric symptoms, depression, upper-limb muscle strength and cognition of older adults with cognitive impairment in long-term care (LTC) facilities. Because of population ageing, the number of older adults with cognitive impairment has been increasing. The effectiveness of medications in treating cognitive impairment is limited; therefore, the global trend has been for non-pharmacological treatments. However, intervention studies of MFPs on older LTC residents with cognitive impairment are scant. This study adopted a quasi-experimental design in accordance with the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) checklist. In total, 84 older adults with cognitive impairment were recruited from seven LTC facilities. The MFP was implemented from July-November 2019. The intervention group received the MFP, which was 50min per session, twice a week for 12weeks, whereas the comparison group received usual care. Neuropsychiatric inventory nursing home version, the Cornell scale for depression in dementia Chinese version, 30-s arm curl test and Mini-Mental State Examination were the outcome measures. Data were analysed using Chi-square test, t test and repeated measure analysis of variance. After the intervention, the scores of neuropsychiatric inventory and the Cornell depression exhibited significant differences over time between two groups (F=3.6, p=.029; F=5.96, p=.003, respectively). Nevertheless, 30-s arm curl test and Mini-Mental State Examination demonstrated non-significant between-group differences. The MFP can effectively reduce neuropsychiatric symptoms and depression in older adults with cognitive impairment in LTC facilities. To ensure a more robust evidence base, more research is warranted. The MFP is a non-pharmacological treatment that can be implemented to promote psychological well-being among older adults with cognitive impairment in LTC facilities, and to indirectly reduce nursing staff burden. To assist nursing staff in providing this intervention, on-the-job training is needed.

Stratification for Identification of Prognostic Categories In the Acute RESpiratory Distress Syndrome (SPIRES) Score.

To develop a scoring model for stratifying patients with acute respiratory distress syndrome into risk categories (Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score) for early prediction of death in the ICU, independent of the underlying disease and cause of death. A development and validation study using clinical data from four prospective, multicenter, observational cohorts. A network of multidisciplinary ICUs. One-thousand three-hundred one patients with moderate-to-severe acute respiratory distress syndrome managed with lung-protective ventilation. None. The study followed Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines for prediction models. We performed logistic regression analysis, bootstrapping, and internal-external validation of prediction models with variables collected within 24 hours of acute respiratory distress syndrome diagnosis in 1,000 patients for model development. Primary outcome was ICU death. The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score was based on patient's age, number of extrapulmonary organ failures, values of end-inspiratory plateau pressure, and ratio of Pao2 to Fio2 assessed at 24 hours of acute respiratory distress syndrome diagnosis. The pooled area under the receiver operating characteristic curve across internal-external validations was 0.860 (95% CI, 0.831-0.890). External validation in a new cohort of 301 acute respiratory distress syndrome patients confirmed the accuracy and robustness of the scoring model (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.829-0.911). The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score stratified patients in three distinct prognostic classes and achieved better prediction of ICU death than ratio of Pao2 to Fio2 at acute respiratory distress syndrome onset or at 24 hours, Acute Physiology and Chronic Health Evaluation II score, or Sequential Organ Failure Assessment scale. The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score represents a novel strategy for early stratification of acute respiratory distress syndrome patients into prognostic categories and for selecting patients for therapeutic trials.

Predicting short and long-term mortality after acute ischemic stroke using EHR

ObjectiveDespite improvements in treatment, stroke remains a leading cause of mortality and long-term disability. In this study, we leveraged administrative data to build predictive models of short- and long-term post-stroke all-cause-mortality. MethodsThe study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. We used patient-level data from electronic health records, three algorithms, and six prediction windows to develop models for post-stroke mortality. ResultsWe included 7144 patients from which 5347 had survived their ischemic stroke after two years. The proportion of mortality was between 8%(605/7144) within 1-month, to 25%(1797/7144) for the 2-years window. The three most common comorbidities were hypertension, dyslipidemia, and diabetes. The best Area Under the ROC curve(AUROC) was reached with the Random Forest model at 0.82 for the 1-month prediction window. The negative predictive value (NPV) was highest for the shorter prediction windows – 0.91 for the 1-month – and the best positive predictive value (PPV) was reached for the 6-months prediction window at 0.92. Age, hemoglobin levels, and body mass index were the top associated factors. Laboratory variables had higher importance when compared to past medical history and comorbidities. Hypercoagulation state, smoking, and end-stage renal disease were more strongly associated with long-term mortality. ConclusionAll the selected algorithms could be trained to predict the short and long-term mortality after stroke. The factors associated with mortality differed depending on the prediction window. Our classifier highlighted the importance of controlling risk factors, as indicated by laboratory measures.

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

ObjectiveEvaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. Study design and settingWe conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. ResultsSixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). ConclusionReporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste.

THE DESIGN OF MANAGEMENT INFORMATION SYSTEMS AT JAMI KAUTSAR MOSQUE

This research is motivated by the management of the Jami Al-Kautsar Mosque in Depok which has not been properly recorded and the data storage is scattered. These constraints have the potential for data loss and inaccurate information and a process taking a long time to produce such information. In this study, the data collection techniques used are observation, interviews and literature study. The information system built uses the waterfall method as a software development or Software Development Life Cycle (SDLC) using object-oriented design of the Unified Modeling Language (UML) and testing using Black Box. The process in this study includes the process of managing activities, managing the mosque, inventory, finances and goods (income and expenditure). The results of research implemented by the author on the Management Information System of the Jami Al-Kautsar Mosque is a system that can provide information on the management of activities, mosque administrators (human resources), inventory, finances and goods (income and expenditure) as well as communicative and informative reports so that they can make evaluation materials and transparent accountability reports from the management to all congregations of the Jami Al-Kautsar Mosque. User satisfaction with the evaluation information system uses the five-category Black Box test and produces a good information system. Three categories of Black Box testing are no errors in functions, database access and structure and system performance above 60%.

New Data Sources and Presidential Campaigns

Social media has an undeniable role in presidential campaigns. Starting with Barack Obama’s first presidential campaign in 2008, on one hand, scholars and practitioners have embraced the potential and importance of these platforms. The 2016 presidential elections, on the other hand, raised concerns about social media’s role in democratic processes as debates about how the platforms can sow misinformation have become mainstream. I argue that there has been a positive outcome of such debates: new data sources. Understanding their role—and their probable potential to do “harm”—social media platforms have worked toward increasing transparency in the political advertisements they carry. From Snapchat to Facebook, transparency reports share detailed information on how political groups, including presidential nominees, have utilized their platforms, targeted audiences, and disseminated calls-to-action. In this article, I argue that these transparency attempts will be invaluable data resources for political communication scholars to better explain how voter choice and candidate positioning work within digital media ecology. I answer four sample research questions about 2020 Presidential Elections in the United States to demonstrate the potential of these data sets in shedding light on how issues, identities, and time-relevant variables change political advertising in presidential campaigns.

Publishing in the Academy of Management Journals

Publishing in the Academy of Management Journals

Healthcare Simulation Use to Support Guidelines Implementation: An Integrative Review.

Simulation-based education is a useful teaching and learning strategy that can help to implement guidelines into healthcare settings. Therefore, the purpose of this paper is to collate, synthesize, and analyze the literature focusing on the use of simulation as an educational strategy to support guidelines implementation among healthcare providers (HCPs). Integrative literature review using the methodology proposed by Ganong. Twenty-three articles were selected, the majority (n=19, 82%) used simulation in practice settings and pre- and post-test measurement (n=16, 69%). All studies that assessed simulation effects highlighted that the use of simulation improved the measured outcomes related to guideline implementation. Simulation-based education can be an effective strategy to support guidelines implementation among HCPs, but aspects such as cost involved, time constraints, training of educators, and the HCPs' learning needs can affect its applicability. Future research should focus on more transparent reports related to the guidelines for simulation content, virtual learning, costs of simulation, and measurement of the long-term effects of simulation-based education.

Changes in the Recording of Cash-Based Accounting Methods to Accrual Base

This study aims to determine changes in public sector accounting from cash basis to accrual basis and which one is better than the two bases. This study uses a qualitative method through literature study that combines a number of reliable data sources such as journals, books, articles, legislation, research results and those relevant to the problem being studied, where 12 of the 42 articles were selected and published between the 2013-2018 period. . The change in cash basis reform towards accruals consists of 3 phases, namely the pre-autonomy era, the transition era and the post-regional autonomy era. Of the two bases, the accrual basis is better because the accrual basis produces more transparent accounting reports for the sake of realizing good governance.

External validation of a radiomic signature to predict HPV (p16) status from standard CT images of anal and vulvar cancer patients.

e15502 Background: HPV status of anal and vulvar cancers cannot be predicted by visual inspection as well as for oropharyngeal cancers. Radiomics applied on computed tomography images can extract features that may better characterize the structure and the underlying biology of the tumor. Methods: In this multi-center study, we validated a CT based radiomic signature to predict HPV (p16) status, developed in head &amp; neck cancer, in anal and vulvar cancer patients. The patients cohort was composed of 68 anal cancer patients and 7 vulvar cancer patients, with p16 status determined by immunohistochemistry, while a control cohort was composed of 422 lung cancer patients. The patient cohorts come from 4 different centers (Maastro Clinic - the Netherlands, CHU Liege – Belgium, St Luke’s Hospital – Ireland, Cork University Hospital - Ireland). The primary tumor volume was manually delineated for each patient on axial CT images. Prior to analysis, all images were resampled to isotropic voxels of 2 mm, using linear interpolation. A total of 37 radiomics features were calculated from five groups: tumor intensity, shape, texture, Wavelet and Laplacian of Gaussian. The signature was built using regularized logistic regression [1]. The signature was evaluated according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) and the Radiomics Quality Score (RQS). Results: The signature classified anal and vulvar cancers based on their HPV status (positive or negative), with an AUROC of 0.760 comparable to the performance of the original signature developed in oropharyngeal squamous cell carcinomas (AUROC of 0.764) [1]. The model, tested in the control cohort of lung cancer patients, predicted the HPV positive status of 1% of the patients which is in line with expected European prevalence (0 – 10%). This signature is TRIPOD level 4 (57%) with an RQS of 61%. Conclusions: This study supplies an additional insight into HPV imaging phenotype, providing a proof of concept that molecular information can be inferred from standard medical images by means of radiomics. These preliminary but encouraging results may pave the road for further generalization of CT image features of HPV-related tumors and aid in the optimization of future therapy developments [2]. Reference [1] Ralph TH Leijenaar et al., The British Journal of Radiology 2018 91:1086 [2] Immunotherapy Drug with Two Targets Shows Promise against HPV-Related Cancers - accessed on 12/02/2021 - https://www.cancer.gov/

Reporting of Studies Conducted on Austrian Claims Data.

In healthcare studies, the analysis of claims data is gaining an increasingly important role. Observational studies should be reported in a manner that promotes internal and external validity assessment, with the exact and standardized description of items. Several international guidelines and checklists for reporting on secondary data are available. The aim of this work was to analyse the applicability of reporting guidelines especially for claims data. The STROSA-2 guidelines recommendations were evaluated by means of a report on a study on triptan medications in Austria. Six items were identified which could be expanded to support complete and transparent report on Austrian claims data. Therefore, we would suggest to add some details in the STROSA-2 guidelines concerning study design, legal foundations, data protection, data flow, descriptive results and risk of bias. The guidelines for reporting on Austrian claims data were successfully compiled with additional items. New guidelines should be further processed and tested with strong recommendations to focus on data limitations and legal aspects.

Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.

Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence. To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery. We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status . We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model. Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews. We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model. Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

Predicting falls in community-dwelling older adults: a systematic review of prognostic models

ObjectiveTo systematically review and critically appraise prognostic models for falls in community-dwelling older adults.Eligibility criteriaProspective cohort studies with any follow-up period. Studies had to develop or validate multifactorial prognostic models...

Mismatch between midline shift and hematoma thickness as a prognostic factor of mortality in patients sustaining acute subdural hematoma

BackgroundAcute subdural hematoma (ASDH) is a traumatic lesion commonly found secondary to traumatic brain injury. Radiological findings on CT, such as hematoma thickness (HT) and structures midline shift (MLS), have...

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

The Effect of Whole-Body Vibration Training on Biomarkers and Health Beliefs of Prefrail Older Adults

Frailty poses a considerable risk to the performance of daily living activities, affecting the quality of life of older adults and increasing their risks of falling, disability, hospitalization, and mortality. Frailty is related to pathology with a state of negative energy balance, sarcopenia, diminished muscle strength, and low tolerance for exertion. Few intervention studies have been conducted on prefrail community-dwelling older adults. This study investigated the effects of whole-body vibration training on the biomarkers and health beliefs of prefrail community-dwelling older adults. We conducted a quasi-experimental research. Ninety participants were allocated to either an experimental or control group. This study followed the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) guidelines for quasi-experimental research design. The experimental group participated in a 12-week whole-body vibration training conducted three times per week, with 5-min continuous vibration at 12-Hz frequency and 3-mm amplitude each time. DVDs on resistance training and manuals were provided to the control group. The data were analysed through descriptive analysis, a chi-square test, an independent t test, a paired t test, and analysis of covariance. There were 42 people in the experimental group and 48 people in the control group. After the 12-week training and intervention, ANCOVA is used to analyze the experimental and control group’s results. Experimental results in 30s chair stand test (F = 98.85, p &lt; 0.05), open-eye stand on dominant foot test (F = 26.15, p &lt; 0.05), two–minute-step test (F = 25.89, p &lt; 0.05), left-leg bone mineral density (F = 14.71, p &lt; 0.05), barriers of perceived frailty (F = 39.81, p &lt; 0.05) and cues to action (F = 92.96, p &lt; 0.05) in health beliefs are statistically significant compared to the control group’s results. The 12-week whole-body vibration program employed in this study partly improved the biomarkers and health beliefs of prefrail community-dwelling older adults. We recommend whole-body vibration training as an exercise strategy, thereby improving community-dwelling older adults’ biomarkers and health beliefs.

An application of machine learning based on real-world data: Mining features of fibrinogen in clinical stages of lung cancer between sexes.

BackgroundLung cancer is the most threatening malignant tumor to human health and life. Using a variety of machine learning algorithms and statistical analyses, this paper explores, discovers and demonstrates new indicators for the early diagnosis of lung cancer and their diagnostic performance from large samples of clinical data in the real world.MethodsBy applying machine learning methods, including minimum description length (MDL), naive Bayesian (NB), K-means (KM), nonnegative matrix factorization (NMF), and decision tree (DT), based on large sample data of 2,502 patients, we built a classification model and systematically explored differences in fibrinogen levels in different clinical stages of lung cancer between the sexes. We also validated the reliability of the model by testing it on a validation cohort of 447 patients. This report adheres to the “Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis” (TRIPOD) statement for the reporting of prediction models.ResultsThe analysis revealed significant differences in fibrinogen levels, pleural effusion, chlorine levels, A-G ratio, glutamic-oxaloacetic transaminase and alkaline phosphatase levels as well as in sex composition between the early-stage lung cancer group and the middle-late-stage lung cancer group. The classification model created by the combination of fibrinogen, alkaline phosphatase and sex demonstrated good performance with an AUC of 73.5%. In addition, in males, a fibrinogen level of 2.94 g/L could initially serve as the upper limit for determining the early-stage lung cancer group, but a level of 3.91 g/L could be preliminarily used as a reference threshold for the lower limit for middle- to late-stage lung cancer. This latter level could also serve as the upper limit of the critical value for early-stage lung cancer in females.ConclusionsAn integrated application based on supervised and unsupervised machine learning algorithms could effectively explore the potential links contained in the clinical data and reveal the differences in fibrinogen levels in different clinical stages of lung cancer between the sexes, which could provide a new reference basis for lung cancer staging.

Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications

PurposeTo evaluate the quality of radiomics studies on meningiomas, using a radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and the Image Biomarker Standardization Initiative (IBSI). MethodsPubMed MEDLINE and Embase were searched to identify radiomics studies on meningiomas. Of 138 identified articles, 25 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and items in IBSI. ResultsOnly four studies (16 %) performed external validation. The mean RQS was 5.6 out of 36 (15.4 %), and the basic adherence rate was 26.8 %. The adherence rate was low for stating biological correlation (4%), conducting calibration statistics (12 %), multiple segmentation (16 %), and stating potential clinical utility (16 %). None of the studies conducted a test‒retest or phantom study, stated a comparison to a ‘gold standard’, conducted prospective studies or cost-effectivity analysis, or opened code and data to the public, resulting in low RQS. The overall adherence rate for TRIPOD was 54.1 %, with low scores for reporting the title (4%), abstract (0%), blind assessment of the outcome (8%), and explaining the sample size (0%). According to IBSI items, only 6 (24 %), 6 (24 %), and 3 (12 %) studies performed N4 bias-field correction, isovoxel resampling, and grey-level discretization, respectively. No study performed skull stripping. ConclusionThe quality of radiomics studies for meningioma is insufficient. Acknowledgement of RQS, TRIPOD, and IBSI reporting guidelines may improve the quality of meningioma radiomics studies and enable their clinical application.

Determinants of Corporate Anti-Corruption Disclosure: The Case of the Emerging Economics

Corruption is a key factor that affects countries’ development, with emerging countries being a geographical area in which it tends to generate greater negative effects. However, few empirical studies analyze corruption from the point of view of disclosure by companies in this relevant geographical area. Based on a regression analysis using data from the 96 large companies from 15 emerging countries included in the 2016 International Transparency Report, this paper seeks to understand what determinants affect such disclosure. In that context, this paper provides empirical evidence to understand the factors that influence reporting on anti-corruption mechanisms in an area of high economic importance that has been little studied to date, pointing to the positive effect of press freedom in a country where the company is located and with the industry being the unique control variable that strengthens this relationship.