BACKGROUND: Quality systematic reviews (SRs) are essential in the practice of evidence-based dermatology. We assessed the methodologic quality of SRs in dermatology from the Philippines. METHODS: We searched databases (MEDLINE, CDSR, PROSPERO, HERDIN; from inception until June 30, 2019), and secondary sources. We included SRs, authored by Filipino primary authors, which included clinical trials on any intervention for the treatment or prevention of a dermatologic disease or for maintenance of healthy skin, hair or nails. Two reviewers independently extracted data and appraised the methodological quality of each included SR using the AMSTAR 2. The 16-item AMSTAR 2 has 7 critical items and 9 non-critical items. The number of critical items mainly determine the overall confidence in the results of the review. Descriptive analysis using means and standard deviation for continuous data, and frequency and percentage distribution for categorical data were employed. RESULTS: Twenty SRs were included in this review, and were mostly published in the 2010s. Majority of SRs had three authors, who belonged to a single institution, with at least one dermatologist. The most common topic was infections and both oral and topical interventions were used. Majority had 5 included studies in the SRs, with a median number of 425 participants. The median number of critical flaws in the included SRs was 4.5, and non-critical flaws, 5. Overall confidence was critically low in majority (19/20 ) of included reviews, with only one review rated as low. CONCLUSION: The methodologic quality of the dermatology SRs from the Philippines based on the AMSTAR 2 tool was poor with a rating of critically low in majority. There is a need to improve quality of conduct and reporting through dissemination of the reporting guidelines such as the PRISMA
Humans ; Nails ; Philippines ; Dermatologists ; Dermatology ; Data Management ; Data Collection ; Publications ; Maintenance ; Reference Standards

Objective: Chronic obstructive pulmonary disease, asthma, interstitial lung disease and pulmonary thromboembolism are the most common and severe respiratory diseases, which seriously jeopardizing the health of the Chinese citizens. Large-scale prospective cohort studies are needed to explore the relationships between potential risk factors and respiratory disease outcomes and to observe disease prognoses through long-term follow-ups. We aimed to develop a common data model (CDM) for cohort studies on respiratory diseases, in order to harmonize and facilitate the exchange, pooling, sharing, and storing of data from multiple sources to serve the purpose of reusing or uniforming those follow-up data appeared in the cohorts. Methods: The process of developing this CDM of respiratory diseases would follow the steps as: ①Reviewing the international standards, including the Clinical Data Interchange Standards Consortium (CDISC), Clinical Data Acquisition Standards Harmonization (CDASH) and the Observational Medical Outcomes Partnership (OMOP) CDM; ②Summarizing four cohort studies of respiratory diseases recruited in this research and assessing the data availability; ③Developing a CDM related to respiratory diseases. Results: Data on recruited cohorts shared a few similar domains but with various schema. The cohorts also shared homogeneous data collection purposes for future follow-up studies, making the harmonization of current and future data feasible. The derived CDM would include two parts: ①thirteen common domains for all the four cohorts and derived variables from disparate questions with a common schema, ②additional domains designed upon disease-specific research needs, as well as additional variables that were disease-specific but not initially included in the common domains. Conclusion: Data harmonization appeared essential for sharing, comparing and pooled analyses, both retrospectively and prospectively. CDM was needed to convert heterogeneous data from multiple studies into one harmonized dataset. The use of a CDM in multicenter respiratory cohort studies would make the constant collection of uniformed data possible, so to guarantee the data exchange and sharing in the future.
Data Collection/standards* ; Databases, Factual/standards* ; Delivery of Health Care/organization & administration* ; Humans ; Information Dissemination ; Pulmonary Disease, Chronic Obstructive

Postoperative complications are important outcome measurements for surgical quality and safety control. However, the complication registration has always been problematic due to the lack of definition consensus and the other practical difficulties. This narrative review summarizes the data registry system for single institutional registry, national data registry, international multi-center trial registries in the western world, aiming to share the experience of complication classification and data registration. We interviewed Dr. Koh from Royal Prince Alfred Hospital in Australia for single institutional experience, Dr. van der Wielen and Dr. Desideriofor, from two international multi-center trial(STOMACH) and registry (IMIGASTRIC) respectively, and Prof. Dr. Wijnhoven from the Dutch Upper GI Audit(DUCA). The major questions include which complications are obligated to report in the respective registry, what are the definitions of those complications, who perform the registration, and how are the complications evaluated or classified. Four telephone conferences were initiated to discuss the above-mentioned topics. The DUCA and IMGASTRIC provided the definition of the major complications. The consent definition provided by DUCA was based on the LOW classification which came out after a four-year discussion and consensus meeting among international experts in the according field. However, none of the four registries asked for an obligatory standardization of the diagnostic criteria among the participating centers or surgeons. Instead, all the registries required a detailed recording of the diagnostic strategy and classification of the complications with the Clavien-Dindo scoring system. Most data were registered by surgeons or data managers during or immediately after the hospitalization. The investigators or an independent third party conducted the auditing of the data quality. Standardization of complication diagnosis among different centers is a difficult task, consuming much effort and time. On top of that, standardization of the complication registration is of critical and practical importance. We encourage all centers to register complications with the diagnostic criteria and following intervention. Based on this, the Clavien-Dindo classification can be properly justified, which has been widely accepted by most centers and should be routinely used as the standard evaluation system for postoperative complications in gastric tumor surgery.
Australia ; epidemiology ; Data Collection ; standards ; statistics & numerical data ; Diagnostic Techniques and Procedures ; standards ; statistics & numerical data ; Digestive System Surgical Procedures ; adverse effects ; statistics & numerical data ; Health Care Surveys ; Humans ; Netherlands ; epidemiology ; Postoperative Complications ; classification ; diagnosis ; epidemiology ; Registries ; standards ; Risk Assessment ; methods ; standards ; Stomach Neoplasms ; complications ; surgery

Complications significantly postpone postoperative recovery and increase mortality after gastric cancer surgery. However, varied complication rates have been reported in the literature as well as in our annual report of China Gastrointestinal Oncological Surgery League. It appears that there have been some misunderstandings regarding the diagnosis and interventions as well as the etiology of major complications. We should be aware of the fact that reaching worldwide consensus for the diagnostic criteria is a difficult task in most complications, which disabled the possibility of data comparison among different studies in most cases. Disagreements among the Chinese and western surgeons in the complication diagnosis should also be acknowledged when interpreting data from both ends. The western colleagues appreciate more latent manifestations of the complications, which eventually lead to earlier and more aggressive examinations and interventions. We, together with all Chinese centers, need to acknowledge the differences, ensure the integrity of our reported data while proposing respective countermeasures. Although the complication registration may continue to differ among centers, we can register complications with the intervention details so that the reported complications can be classified with the same standard (i.e. Clavien-Dindo scoring system). We should also encourage multi-center collaborations and trials, which facilitate the standardization of complication diagnosis and registration. Recognizing the difficulty and long-course nature of reaching national or even international consensus, we continue aiming at the ultimate goal with our best effort.
China ; Consensus ; Data Collection ; standards ; Diagnostic Techniques and Procedures ; standards ; Digestive System Surgical Procedures ; adverse effects ; Humans ; Postoperative Complications ; classification ; epidemiology ; Registries ; standards ; statistics & numerical data ; Stomach Neoplasms ; surgery

There is no a systemic performance metrics for clinical data management. While the CDMC in China starts to develop the quality metrics for clinical data management, it is essential to think over the performance and pursue metrics implementation of clinical data management in China. This article provides the basic concept, development and implementation of the performance metric in clinical data management.
China ; Clinical Trials as Topic ; standards ; Data Collection ; standards ; Information Storage and Retrieval ; standards

This paper is prepared to discuss the common issues in data management, such as building and training of data management team, standard operation procedure, document management, execution and communication, strategies to correct and prevent mistakes, and measures to improve the quality and efficiency of clinical trials and data management.
Clinical Trials as Topic ; Data Collection ; standards ; Information Storage and Retrieval ; standards

For pharmaceutical industries, clinical data is one of the most valuable deliverables. It is also the basis of analysis, submission, approval, labeling and marketing of a drug product. To ensure the integrity and reliability of clinical data, a scientific standardized quality control (QC) has to be established at each step of a clinical trial. Data validation is conducted to ensure the reasonability and compliance of clinical data by checking data quality before the data is statistically analyzed. This paper focuses on purpose of data validation, creation of data validation plan, rationale of data validation, types of data validation and performance of user acceptance testing on clinical database.
Clinical Trials as Topic ; standards ; Data Accuracy ; Data Collection ; standards ; Databases, Factual ; Quality Control ; Reproducibility of Results

With the wide application of electronic data management (EDC), the data management is shifting to a new mode. In order to recognize the advantages of EDC, we choose 20 representative registered clinical trials, which involve 5 404 subjects and 321 sites. We found that EDC has many beneficial impacts on the course of clinical trial data management, including the process of data collection, data cleaning, data quality control and clinical trial decision-making. The result also provides a reference for the adoption of EDC in clinical trials.
Clinical Trials as Topic ; Data Collection ; standards ; Information Storage and Retrieval ; standards ; Quality Control

Due to a great amount of data in clinical trials, the data cleansing needs to adopt a variety of measures, including the latest developed visual check approach. According to the different types of clinical data and the different stages in the course of clinical data management, this study reviews 8 types of visual graphics that show the relevance and trend among the data. The series of graphics can rapidly detect abnormal data, monitor clinical research in real-time, make the data management process much easier and improve the clinical trial efficiency and data quality.
Clinical Trials as Topic ; standards ; Data Collection ; standards ; Information Storage and Retrieval ; methods

Case report form (CRF) is a key document for data collection in clinical trials. A well-designed CRF is required for database construction, data accuracy, data query/cleaning, CRF completion and statistical analysis. A well-defined process or SOP should be in place for CRF design. Data collection should fully meet the demand of study protocol. The layout of CRF should be clear with well-structured fields and standard coding for fields.
Clinical Trials as Topic ; standards ; Data Collection ; standards ; Documentation ; standards ; Research Design ; standards