Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post-operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most-widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions.

BACKGROUND: Complex and expanding data sets in clinical oncology applications require flexible and interactive visualization of patient data to provide the maximum amount of information to physicians and other medical practitioners. Interdisciplinary tumor conferences in particular profit from customized tools to integrate, link, and visualize relevant data from all professions involved.
OBJECTIVE: The scoping review proposed in this protocol aims to identify and present currently available data visualization tools for tumor boards and related areas. The objective of the review will be to provide not only an overview of digital tools currently used in tumor board settings, but also the data included, the respective visualization solutions, and their integration into hospital processes.
METHODS: The planned scoping review process is based on the Arksey and O\'Malley scoping study framework. The following electronic databases will be searched for articles published in English: PubMed, Web of Knowledge, and SCOPUS. Eligible articles will first undergo a deduplication step, followed by the screening of titles and abstracts. Second, a full-text screening will be used to reach the final decision about article selection. At least 2 reviewers will independently screen titles, abstracts, and full-text reports. Conflicting inclusion decisions will be resolved by a third reviewer. The remaining literature will be analyzed using a data extraction template proposed in this protocol. The template includes a variety of meta information as well as specific questions aiming to answer the research question: \"What are the key features of data visualization solutions used in molecular and organ tumor boards, and how are these elements integrated and used within the clinical setting?\" The findings will be compiled, charted, and presented as specified in the scoping study framework. Data for included tools may be supplemented with additional manual literature searches. The entire review process will be documented in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart.
RESULTS: The results of this scoping review will be reported per the expanded PRISMA-ScR guidelines. A preliminary search using PubMed, Web of Knowledge, and Scopus resulted in 1320 articles after deduplication that will be included in the further review process. We expect the results to be published during the second quarter of 2024.
CONCLUSIONS: Visualization is a key process in leveraging a data set\'s potentially available information and enabling its use in an interdisciplinary setting. The scoping review described in this protocol aims to present the status quo of visualization solutions for tumor board and clinical oncology applications and their integration into hospital processes.
UNASSIGNED: DERR1-10.2196/53627.

UNASSIGNED: Imaging plays a pivotal role in eye assessment. With the introduction of advanced machine learning and artificial intelligence (AI), the focus has shifted to imaging datasets in ophthalmology. While disparities and health inequalities hidden within data are well-documented, the ophthalmology field faces specific challenges to the creation and maintenance of datasets. Optical Coherence Tomography (OCT) is useful for the diagnosis and monitoring of retinal pathologies, making it valuable for AI applications. This review aims to identify and compare the landscape of publicly available optical coherence tomography databases for AI applications.
UNASSIGNED: We conducted a literature review on OCT and AI articles with publicly accessible datasets, using PubMed, Scopus, and Web of Science databases. The review retrieved 183 articles, and after full-text analysis, 50 articles were included. From the included articles were identified 8 publicly available OCT datasets, focusing on patient demographics and clinical details for thorough assessment and comparison.
UNASSIGNED: The resulting datasets encompass 154,313 images collected from Spectralis, Cirrus HD, Topcon 3D, and Bioptigen devices. These datasets included normal exams, age-related macular degeneration, and diabetic maculopathy, among others. Comprehensive demographic information is available in one dataset and the USA is the most represented population.
UNASSIGNED: Current publicly available OCT databases for AI applications exhibit limitations, stemming from their non-representative nature and the lack of comprehensive demographic information. Limited datasets hamper research and equitable AI development. To promote equitable AI algorithmic development in ophthalmology, there is a need for the creation and dissemination of more representative datasets.

BACKGROUND: Hyaluronidase (Hyal) can reverse complications of hyaluronic acid (HA) fillers, which has contributed substantially to the popularity of such procedures. Still, there are differing opinions regarding Hyal treatment, including dosage recommendations in filler complication management.
OBJECTIVE: We aimed to address unanswered questions regarding Hyal treatment for HA filler complications, including timing and dosage, skin pretesting, properties of various Hyals and interactions with HA gels, and pitfalls of the treatment.
METHODS: PubMed and Google Scholar databases were searched from inception for articles on Hyal therapy for filler complications. Articles were evaluated regarding their contribution to the field. The extensive literature review includes international leaders\' suggestions and expert panels\' recommendations.
RESULTS: There are limited controlled data but increasing clinical experience with Hyal treatment. The currently used Hyals provide good results and have an acceptable safety profile. Nonemergent complications such as the Tyndall effect, noninflamed nodules, and allergic or hypersensitivity reactions should be treated with low or moderate Hyal doses. Hyal should be considered with prior or simultaneous oral antibiotic treatment in managing inflammatory nodules. Hyal may be tried for granulomas that have not responded to intralesional steroids. Emergent complications such as vascular occlusion and blindness require immediate, high-dose Hyal treatment. Regarding blindness, the injection technique, retrobulbar versus supraorbital, remains controversial. Ultrasound guidance can increase the efficacy of the above interventions.
CONCLUSIONS: Hyal is essential in aesthetic practice because it can safely treat most HA filler complications. Immediate Hyal treatment is required for emergent complications. Aesthetic practitioners should be versed in using Hyal and effective dosage protocols.

BACKGROUND: Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine.
METHODS: This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form.
RESULTS: Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness.
CONCLUSIONS: There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards.

Artificial Intelligence (AI) technology has been developed significantly in recent years. The fairness of medical AI is of great concern due to its direct relation to human life and health. This review aims to analyze the existing research literature on fairness in medical AI from the perspectives of computer science, medical science, and social science (including law and ethics). The objective of the review is to examine the similarities and differences in the understanding of fairness, explore influencing factors, and investigate potential measures to implement fairness in medical AI across English and Chinese literature.
This study employed a scoping review methodology and selected the following databases: Web of Science, MEDLINE, Pubmed, OVID, CNKI, WANFANG Data, etc., for the fairness issues in medical AI through February 2023. The search was conducted using various keywords such as \"artificial intelligence,\" \"machine learning,\" \"medical,\" \"algorithm,\" \"fairness,\" \"decision-making,\" and \"bias.\" The collected data were charted, synthesized, and subjected to descriptive and thematic analysis.
After reviewing 468 English papers and 356 Chinese papers, 53 and 42 were included in the final analysis. Our results show the three different disciplines all show significant differences in the research on the core issues. Data is the foundation that affects medical AI fairness in addition to algorithmic bias and human bias. Legal, ethical, and technological measures all promote the implementation of medical AI fairness.
Our review indicates a consensus regarding the importance of data fairness as the foundation for achieving fairness in medical AI across multidisciplinary perspectives. However, there are substantial discrepancies in core aspects such as the concept, influencing factors, and implementation measures of fairness in medical AI. Consequently, future research should facilitate interdisciplinary discussions to bridge the cognitive gaps between different fields and enhance the practical implementation of fairness in medical AI.

BACKGROUND: To support public health researchers and advocates seeking to challenge the influence of powerful commercial actors on health, it is necessary to develop a deeper understanding of corporate political activities. This project explores political science scholarship analysing lobbying to identify new datasets and research methods that can be applied to public health and stimulate further research and advocacy.
METHODS: We undertook a systematic scoping review of peer-reviewed and grey literature reports analysing the practice of lobbying. Titles and abstracts of 4533 peer-reviewed and 285 grey literature reports were screened, with 233 peer-reviewed and 280 grey literature reports assessed for eligibility. We used a two-stage process for data extraction. In stage 1, we collected two pieces of information from all included studies: data sources and indicators used to measure lobbying. For the second stage, data extraction was limited to 15 studies that focused on meetings.
RESULTS: The most common indicators used to measure lobbying activity were: registrations of active lobbyists; expenditure on lobbying; meetings; written comments and submissions made to government consultations; bills; and committee participation. A range of different data sources were used to analyse lobbying, including from governments, not-for-profits and commercial sources. All 15 studies analysing lobbyist meetings were from high-income contexts. The studies analysed three key variables: the types of government actors targeted by lobbying; the policies of interest; and the lobbyists and/or their clients. The studies used a range of taxonomies to classify policy issues and the types of actors engaged in lobbying. All studies discussed challenges with accessing and analysing lobbying data.
CONCLUSIONS: There is enormous potential for public health research and advocacy concerned with commercial lobbying to learn from political science scholarship. This includes both conceptual frameworks and sources of empirical data. Moreover, the absence of good quality transparency internationally emphasises the importance of advocacy to support policy change to improve the quality of political transparency to make it easier to monitor commercial lobbying.

Digital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the \"measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.\"
This scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle.
We performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform.
We retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners\' interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively.
We identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course\'s run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed.

Measurement is critical for assessing and monitoring food security. Yet, it is difficult to comprehend which food security dimensions, components, and levels the numerous available indicators reflect. We thus conducted a systematic literature review to analyse the scientific evidence on these indicators to comprehend the food security dimensions and components covered, intended purpose, level of analysis, data requirements, and recent developments and concepts applied in food security measurement. Data analysis of 78 articles shows that the household-level calorie adequacy indicator is the most frequently used (22%) as a sole measure of food security. The dietary diversity-based (44%) and experience-based (40%) indicators also find frequent use. The food utilisation (13%) and stability (18%) dimensions were seldom captured when measuring food security, and only three of the retrieved publications measured food security by considering all the four food security dimensions. The majority of the studies that applied calorie adequacy and dietary diversity-based indicators employed secondary data whereas most of the studies that applied experience-based indicators employed primary data, suggesting the convenience of collecting data for experience-based indicators than dietary-based indicators. We confirm that the estimation of complementary food security indicators consistently over time can help capture the different food security dimensions and components, and experience-based indicators are more suitable for rapid food security assessments. We suggest practitioners to integrate food consumption and anthropometry data in regular household living standard surveys for more comprehensive food security analysis. The results of this study can be used by food security stakeholders such as governments, practitioners and academics for briefs, teaching, as well as policy-related interventions and evaluations.
UNASSIGNED: The online version contains supplementary material available at 10.1186/s40066-023-00415-7.

BACKGROUND: Opioid use disorder (OUD) during pregnancy presents a significant risk to maternal, fetal, and neonatal health, increasing the likelihood of adverse events, such as maternal overdose, pregnancy loss, stillbirth, preterm birth, low birth weight, and neonatal abstinence syndrome. In order to reduce the risk of these outcomes, the standard of care for OUD during pregnancy in many jurisdictions within the United States and Canada is opioid agonist therapy (OAT). OAT refers to prescription medications that alleviate or eliminate opioid withdrawal symptoms, so that opioid use can be managed more safely. Although OAT has been recognized as a safe option for pregnant people with OUD, many jurisdictions do not have treatment guidelines regarding pharmacological options, dosing recommendations, side effect management, and individual preferences. There is currently a lack of systematic evidence on the impacts of different OAT regimens on pregnancy outcomes.
OBJECTIVE: We aim to evaluate the impacts of specific OAT agents on pregnancy outcomes and inform recommendations for practitioners treating pregnant people with OUD.
METHODS: The MEDLINE, Embase, CINAHL, and PsycINFO databases will be searched for published quantitative studies assessing pregnancy outcomes for individuals on OAT. Given the substantially increased risk of preterm birth, low birth weight, small for gestational age, and stillbirth among pregnant people with OUD, these four end points will comprise our primary outcomes. Database searches will not be restricted by date, and conference abstracts will be restricted to the past 2 years. Titles, abstracts, and full-text articles will be independently screened by 2 reviewers. Data will be extracted independently and in duplicate, using a data extraction form to reduce the risk of reviewer bias. The risk of bias within individual studies will be assessed by using the appropriate CASP (Critical Appraisal Skills Programme) checklists. For studies that consider the same research questions, interventions, or outcomes, meta-analyses will be conducted to synthesize the pooled effect size. In the event that studies cannot be compared directly, results will be synthesized in a narrative account. Between-study heterogeneity will be measured by using the τ2 statistic. If more than 10 studies are available for pooling, publication bias will be evaluated by using the Egger regression test.
RESULTS: As of January 2023, a total of 3266 abstracts have been identified for screening. Data extraction is expected to commence in February 2023.
CONCLUSIONS: The topic of OAT and its effect on pregnancy is an understudied area that has the potential to improve health outcomes, clinical practice, education, and community advocacy. The results of our review will be used to inform clinical practice guidelines and improve health outcomes for pregnant people. Findings will be disseminated to diverse groups of stakeholders, including policy makers, clinicians, community partners, and individuals with lived experience of drug use.
BACKGROUND: PROSPERO CRD42022332082; https://tinyurl.com/2p94pkx5.
UNASSIGNED: DERR1-10.2196/42417.