lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses.

BACKGROUND: The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards. METHODS: We developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. RESULTS: lab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm's efficacy in handling large-scale data sets. CONCLUSIONS: lab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management.

Distributed Ledger-Based System to Support Health Data Integrity and Transparency.

This article addresses critical health data integrity by proposing an HF (Hyperledger Fabric)-based architecture with integration into the global health data architecture based on distributed content-addressable storage networks.

Blockchain-Based Caching Architecture for DApp Data Security and Delivery.

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model's effectiveness in providing secure and high-performance DApp solutions.

Three levels of discrepancies in the records of trial sites in India, registered with the European Union Clinical Trials Register.

Introduction: Clinical trial registries serve a key role in tracking the trial enterprise. We are interested in the record of trials sites in India. In this study, we focused on the European Union Clinical Trial Registry (EUCTR). This registry is complex because a given study may have records from multiple countries in the EU, and therefore a given study ID may be represented by multiple records. We wished to determine what steps are required to identify the studies that list sites in India that are registered with EUCTR. Methods: We used two methodologies. Methodology A involved downloading the EUCTR database and querying it. Methodology B used the search function on the registry website. Results: Discrepant information, on whether or not a given study listed a site in India, was identified at three levels: (i) the methodology of examining the database; (ii) the multiple records of a given study ID; and (iii) the multiple fields within a given record. In each of these situations, there was no basis to resolve the discrepancy, one way or another. Discussion: This work contributes to methodologies for more accurate searches of trial registries. It also adds to the efforts of those seeking transparency in trial data.

Evaluating the Problem of Fraudulent Participants in Health Care Research: Multimethod Pilot Study.

BACKGROUND: The shift toward online recruitment methods, accelerated by the COVID-19 pandemic, has brought to the forefront the growing concern of encountering fraudulent participants in health care research. The increasing prevalence of this issue poses a serious threat to the reliability and integrity of research data and subsequent findings. OBJECTIVE: This study aims to explore the experiences of health care researchers (HCRs) who have encountered fraudulent participants while using online recruitment methods and platforms. The primary objective was to gain insights into how researchers detect and mitigate fraudulent behavior in their work and provide prevention recommendations. METHODS: A multimethod sequential design was used for this pilot study, comprising a quantitative arm involving a web-based survey followed by a qualitative arm featuring semistructured interviews. The qualitative description approach framed the qualitative arm of the study. Sample sizes for the quantitative and qualitative arms were based on pragmatic considerations that in part stemmed from encountering fraudulent participants in a concurrent study. Content analysis was used to analyze open-ended survey questions and interview data. RESULTS: A total of 37 HCRs participated, with 35% (13/37) of them engaging in qualitative interviews. Online platforms such as Facebook, email, Twitter (subsequently rebranded X), and newsletters were the most used methods for recruitment. A total of 84% (31/37) of participants indicated that fraudulent participation occurred in studies that mentioned incentives in their recruitment communications, with 71% (26/37) of HCRs offering physical or electronic gift cards as incentives. Researchers identified several indicators of suspicious behavior, including email surges, discrepancies in contact or personal information, geographical inconsistencies, and suspicious responses to survey questions. HCRs emphasized the need for a comprehensive screening protocol that extends beyond eligibility checks and is seamlessly integrated into the study protocol, grant applications, and research ethics board submissions. CONCLUSIONS: This study sheds light on the intricate and pervasive problem of fraudulent participation in health care research using online recruitment methods. The findings underscore the importance of vigilance and proactivity among HCRs in identifying, preventing, and addressing fraudulent behavior. To effectively tackle this challenge, researchers are encouraged to develop a comprehensive prevention strategy and establish a community of practice, facilitating real-time access to solutions and support and the promotion of ethical research practices. This collaborative approach will enable researchers to effectively address the issue of fraudulent participation, ensuring the conduct of high-quality and ethically sound research in the digital age.

Combating Fraudulent Participation in Urban American Indian and Alaska Native Virtual Health Research: Protocol for Increasing Data Integrity in Online Research (PRIOR).

BACKGROUND: While the advantages of using the internet and social media for research recruitment are well documented, the evolving online environment also enhances motivations for misrepresentation to receive incentives or to "troll" research studies. Such fraudulent assaults can compromise data integrity, with substantial losses in project time; money; and especially for vulnerable populations, research trust. With the rapid advent of new technology and ever-evolving social media platforms, it has become easier for misrepresentation to occur within online data collection. This perpetuation can occur by bots or individuals with malintent, but careful planning can help aid in filtering out fraudulent data. OBJECTIVE: Using an example with urban American Indian and Alaska Native young women, this paper aims to describe PRIOR (Protocol for Increasing Data Integrity in Online Research), which is a 2-step integration protocol for combating fraudulent participation in online survey research. METHODS: From February 2019 to August 2020, we recruited participants for formative research preparatory to an online randomized control trial of a preconceptual health program. First, we described our initial protocol for preventing fraudulent participation, which proved to be unsuccessful. Then, we described modifications we made in May 2020 to improve the protocol performance and the creation of PRIOR. Changes included transferring data collection platforms, collecting embedded geospatial variables, enabling timing features within the screening survey, creating URL links for each method or platform of data collection, and manually confirming potentially eligible participants' identifying information. RESULTS: Before the implementation of PRIOR, the project experienced substantial fraudulent attempts at study enrollment, with less than 1% (n=6) of 1300 screened participants being identified as truly eligible. With the modified protocol, of the 461 individuals who completed a screening survey, 381 did not meet the eligibility criteria assessed on the survey. Of the 80 that did, 25 (31%) were identified as ineligible via PRIOR. A total of 55 (69%) were identified as eligible and verified in the protocol and were enrolled in the formative study. CONCLUSIONS: Fraudulent surveys compromise study integrity, validity of the data, and trust among participant populations. They also deplete scarce research resources including respondent compensation and personnel time. Our approach of PRIOR to prevent online misrepresentation in data was successful. This paper reviews key elements regarding fraudulent data participation in online research and demonstrates why enhanced protocols to prevent fraudulent data collection are crucial for building trust with vulnerable populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT04376346; https://www.clinicaltrials.gov/study/NCT04376346. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52281.

Concerns about data integrity across 263 papers by one author.

OBJECTIVE: Comprehensive investigation of published work by authors suspected of academic misconduct can reveal further concerns. We aimed to test for data integrity concerns in papers published by an author with eight retracted articles. STUDY DESIGN: We investigated the integrity of all papers reporting on prospective clinical studies by this author. We assessed the feasibility of study methods, baseline characteristics, and outcomes. We plotted the author's clinical research activity over time. We conducted pairwise comparisons of text, tables, and figures to identify duplicate publications, and checked for consistency between conference abstracts, interim analyses, trial registrations, and final papers. Where indicated, we recalculated p-values from the reported summary statistics. RESULTS: We identified 263 papers claiming to have enrolled 74,667 participants between January 2009 and July 2022, 190 (72%) of which reported on studies that recruited from the Assiut Women's Health Hospital in Assiut, Egypt. The number of active studies per month was greatest between 2016 and 2019, with 88 ongoing studies in May 2017. We found evidence of data integrity concerns in 130 (49%) papers, 43 (33%) of which contained concerns sufficient to suggest that they could not be based on data reliably collected from human participants. CONCLUSION: Our investigation finds evidence of widespread integrity concerns in the collected work of one author. We recommend that the involved journals collaborate in a formal investigation.

Likelihood Ratio Test and the Evidential Approach for 2 × 2 Tables.

Categorical data analysis of 2 × 2 contingency tables is extremely common, not least because they provide risk difference, risk ratio, odds ratio, and log odds statistics in medical research. A χ2 test analysis is most often used, although some researchers use likelihood ratio test (LRT) analysis. Does it matter which test is used? A review of the literature, examination of the theoretical foundations, and analyses of simulations and empirical data are used by this paper to argue that only the LRT should be used when we are interested in testing whether the binomial proportions are equal. This so-called test of independence is by far the most popular, meaning the χ2 test is widely misused. By contrast, the χ2 test should be reserved for where the data appear to match too closely a particular hypothesis (e.g., the null hypothesis), where the variance is of interest, and is less than expected. Low variance can be of interest in various scenarios, particularly in investigations of data integrity. Finally, it is argued that the evidential approach provides a consistent and coherent method that avoids the difficulties posed by significance testing. The approach facilitates the calculation of appropriate log likelihood ratios to suit our research aims, whether this is to test the proportions or to test the variance. The conclusions from this paper apply to larger contingency tables, including multi-way tables.

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis.

BACKGROUND: Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. METHODS: Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. RESULTS: Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. CONCLUSIONS: The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.

Enhancing secure multi-group data sharing through integration of IPFS and hyperledger fabric.

Data sharing is increasingly important across various industries. However, issues such as data integrity verification during sharing, encryption key leakage, and difficulty sharing data between different user groups have been identified. To address these challenges, this study proposes a multi-group data sharing network model based on Consortium Blockchain and IPFS for P2P sharing. This model uses a dynamic key encryption algorithm to provide secure data sharing, avoiding the problems associated with existing data transmission techniques such as key cracking or data leakage due to low security and reliability. Additionally, the model establishes an IPFS network for users within the group, allowing for the generation of data probes to verify data integrity, and the use of the Fabric network to record log information and probe data related to data operations and encryption. Data owners retain full control over access to their data to ensure privacy and security. The experimental results show that the system proposed in this study has wide applicability.

Advancing data honesty in experimental biology.

The ease with which scientific data, particularly certain types of raw data in experimental biology, can be fabricated without trace begs urgent attention. This is thought to be a widespread problem across the academic world, where published results are the major currency, incentivizing publication of (usually positive) results at the cost of lax scientific rigor and even fraudulent data. Although solutions to improve data sharing and methodological transparency are increasingly being implemented, the inability to detect dishonesty within raw data remains an inherent flaw in the way in which we judge research. We therefore propose that one solution would be the development of a non-modifiable raw data format that could be published alongside scientific results; a format that would enable data authentication from the earliest stages of experimental data collection. A further extension of this tool could allow changes to the initial original version to be tracked, so every reviewer and reader could follow the logical footsteps of the author and detect unintentional errors or intentional manipulations of the data. Were such a tool to be developed, we would not advocate its use as a prerequisite for journal submission; rather, we envisage that authors would be given the option to provide such authentication. Only authors who did not manipulate or fabricate their data can provide the original data without risking discovery, so the mere choice to do so already increases their credibility (much like 'honest signaling' in animals). We strongly believe that such a tool would enhance data honesty and encourage more reliable science.

EPOPTIS: A Monitoring-as-a-Service Platform for Internet-of-Things Applications.

The technology landscape has been dynamically reshaped by the rapid growth of the Internet of Things, introducing an era where everyday objects, equipped with smart sensors and connectivity, seamlessly interact to create intelligent ecosystems. IoT devices are highly heterogeneous in terms of software and hardware, and many of them are severely constrained. This heterogeneity and potentially constrained nature creates new challenges in terms of security, privacy, and data management. This work proposes a Monitoring-as-a-Service platform for both monitoring and management purposes, offering a comprehensive solution for collecting, storing, and processing monitoring data from heterogeneous IoT networks for the support of diverse IoT-based applications. To ensure a flexible and scalable solution, we leverage the FIWARE open-source framework, also incorporating blockchain and smart contract technologies to establish a robust integrity verification mechanism for aggregated monitoring and management data. Additionally, we apply automated workflows to filter and label the collected data systematically. Moreover, we provide thorough evaluation results in terms of CPU and RAM utilization and average service latency.

Statistical techniques to assess publication integrity in groups of randomized trials: a narrative review.

OBJECTIVES: To describe statistical tools available for assessing publication integrity of groups of randomized controlled trials (RCTs). STUDY DESIGN AND SETTING: Narrative review. RESULTS: Freely available statistical tools have been developed that compare the observed distributions of baseline variables with the expected distributions that would occur if successful randomization occurred. For continuous variables, the tools assess baseline means, baseline P values, and the occurrence of identical means and/or standard deviation. For categorical variables, they assess baseline P values, frequency counts for individual or all variables, numbers of trial participants randomized or withdrawing, and compare reported with independently calculated P values. The tools have been used to identify publication integrity concerns in RCTs from individual groups, and performed at an acceptable level in discriminating intentionally fabricated baseline summary data from genuine RCTs. The tools can be used when concerns have been raised about RCT(s) from an individual/group and when the whole body of their work is being examined, when conducting systematic reviews, and could be adapted to aid screening of RCTs at journal submission. CONCLUSION: Statistical tools are useful for the assessment of publication integrity of groups of RCTs.

Test facility management: Responsibilities and paradigms of a new era.

GLP test facility management refers to the proper management and organization of a facility that conducts studies according to GLP regulations. Compliance with GLP regulations is necessary for data generated in such facilities to be accepted by regulatory authorities. According to GLP Principles, Test facility management (TFM) is responsible for a wide range of tasks and responsibilities to ensure the smooth and efficient operation of the facility. The framework in which the TFM operates within the Test Facility is certainly much more complex than in the early days of the GLP, and moreover it is unlikely that anything will change from a scientific and technological point of view in the years to come. Several aspects have changed from a scientific and technological point of view, and we know that innovation is very rapid. From the above considerations emerges the need for a major change in the performance of the TFM's role.

Data from the Indian drug regulator and from Clinical Trials Registry-India does not always match.

Introduction: In India, regulatory trials, which require the drug regulator's permission, must be registered with the Clinical Trials Registry-India (CTRI) as of 19 March 2019. In this study, for about 300 trials, we aimed to identify the CTRI record that matched the trial for which the regulator had given permission. After identifying 'true pairs', our goal was to determine whether the sites and Principal Investigators mentioned in the permission letter were the same as those mentioned in the CTRI record. Methods: We developed a methodology to compare the regulator's permission letters with CTRI records. We manually validated 151 true pairs by comparing the titles, the drug interventions, and the indications. We then examined discrepancies in their trial sites and Principal Investigators. Results: Our findings revealed substantial variations in the number and identity of sites and Principal Investigators between the permission letters and the CTRI records. Discussion: These discrepancies raise concerns about the accuracy and transparency of regulatory trials in India. We recommend easier data extraction from regulatory documents, cross-referencing regulatory documents and CTRI records, making public the changes to approval letters, and enforcing oversight by Institutional Ethics Committees for site additions or deletions. These steps will increase transparency around regulatory trials running in India.

The poor reliability of thermal conductivity data in the aerogel literature: a call to action!

Aerogels are an exciting class of materials with record-breaking properties including, in some cases, ultra-low thermal conductivities. The last decade has seen a veritable explosion in aerogel research and industry R&D, leading to the synthesis of aerogels from a variety of materials for a rapidly expanding range of applications. However, both from the research side, and certainly from a market perspective, thermal insulation remains the dominant application. Unfortunately, continued progress in this area suffers from the proliferation of incorrect thermal conductivity data, with values that often are far outside of what is possible within the physical limitations. This loss of credibility in reported thermal conductivity data poses difficulties in comparing the thermal performance of different types of aerogels and other thermal superinsulators, may set back further scientific progress, and hinder technology transfer to industry and society. Here, we have compiled 519 thermal conductivity results from 87 research papers, encompassing silica, other inorganic, biopolymer and synthetic polymer aerogels, to highlight the extent of the problem. Thermal conductivity data outside of what is physically possible are common, even in high profile journals and from the world's best universities and institutes. Both steady-state and transient methods can provide accurate thermal conductivity data with proper instrumentation, suitable sample materials and experienced users, but nearly all implausible data derive from transient methods, and hot disk measurements in particular, indicating that under unfavorable circumstances, and in the context of aerogel research, transient methods are more prone to return unreliable data. Guidelines on how to acquire reliable thermal conductivity data are provided. This paper is a call to authors, reviewers, editors and readers to exercise caution and skepticism when they report, publish or interpret thermal conductivity data.

Implementation of the EQIPD Quality System.

The EQIPD Quality System (QS) was conceptualized and established by an international consortium consisting of academic and industrial partners to ensure that non-regulated biomedical research will be conducted according to Good Research Practice expectations. The QS supports researchers to reflect on and improve internal practices by providing a systematic framework and guidance for implementing the EQIPD QS in a time and cost effective manner. This report describes the content of the EQIPD QS with its key features and 18 Core Requirements (CR) in more detail. It gives a short background on each CR and hands on examples on how they were addressed by two different research labs in their respective laboratory environments. Thereby, this article provides examples and direction for other research labs who aim to implement the QS as well. The final paragraphs discuss the potential benefits of the QS in respect to different user groups and stakeholders within the scientific community and summarize the overall governance structure of the EQIPD framework.

Processes and the Electronic Health Record: Challenges and Difficulties Faced when Creating an OB Quality Dashboard.

Healthcare quality as defined by the National Academy of Medicine is "the degree to which health care services for individuals and populations increase the likelihood of desired health outcomes [1]". While building QI dashboard data quality to improve the maternal health of our patient population issues were discovered that hindered that the progress of the project. This paper will discuss the challenges and difficulties faced while creating an OB quality dashboard at a regional perinatal.

Participant Misrepresentation in Online Focus Groups: Red Flags and Proactive Measures.

Covid-19 public health measures prompted a significant increase in online research. This approach has several benefits over face-to-face data-collection methods, including lower cost and wider geographical reach of participants. Yet when the online data-collection instrument is a survey, there are also well-documented drawbacks of participant misrepresentation and related data-authenticity issues. However, the scholarly literature has not looked at participant misrepresentation in online focus-group empirical research. This case study communicates a concerning situation that arose during our research project: dishonest participant behavior threatened the integrity and validity of our data collected through online focus-group sessions as well as e-surveys. We describe the study context, initial red flags alerting us to the issue, subsequent investigations, and implications for research ethics, funding, and data quality. We conclude with a discussion of potential steps to safeguard future online focus-group research against similar issues.