Aims/Background Blended learning has been a commonly adopted teaching mode in the medical education community in recent years. Many studies have shown that the blended learning mode is superior to the traditional teaching mode. Nonetheless, pinpointing the specific advantages provided by blended teaching methods is challenging, since multiple elements influence their effectiveness. This study aimed to investigate the reliability of the conclusions of published randomised controlled trials (RCTs) on blended learning in medical education by assessing their quality, and to provide suggestions for future related studies. Methods Two investigators searched PUBMED and EMBASE, and assessed RCTs related to medical blended learning published from January 1, 2010 to December 31, 2021. The analysis of the overall quality of each report was based on the 2010 consolidated standard of reporting trials (CONSORT) Statement applying a 28-point overall quality score. We also conducted a multivariate assessment including year of publication, region of the trial, journal, impact factor, sample size, and the primary outcome. Results A total of 22 RCTs closely relevant to medical blended learning were eventually selected for study. The results demonstrated that half of the studies failed to explicitly describe at least 34% of the items in the 2010 CONSORT Statement. Medical blended learning is an emerging new teaching mode, with 95.45% of RCTs published since 2010. However, many issues that we consider crucial were not satisfactorily addressed in the selected RCTs. Conclusion Although the 2010 CONSORT Statement was published more than a decade ago, the quality of RCTs remains unsatisfactory. Some important items were inadequately reported in many RCTs such as sample size, blinding, and concealment. We encourage researchers who focus on the effects of blended learning in medical education to incorporate the guidelines in the 2010 CONSORT Statement when designing and conducting relevant research. Researchers, reviewers, and editors also need to work together to improve the quality of relevant RCTs in accordance with the requirements of the 2010 CONSORT Statement.

BACKGROUND: Artificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient outcomes. AI medical devices have shown potential for a range of clinical tasks such as diagnostics, prognostics, and therapeutic decision-making such as drug dosing. There is, however, an urgent need to ensure that these technologies remain safe for all populations. Recent literature demonstrates the need for rigorous performance error analysis to identify issues such as algorithmic encoding of spurious correlations (eg, protected characteristics) or specific failure modes that may lead to patient harm. Guidelines for reporting on studies that evaluate AI medical devices require the mention of performance error analysis; however, there is still a lack of understanding around how performance errors should be analyzed in clinical studies, and what harms authors should aim to detect and report.
OBJECTIVE: This systematic review will assess the frequency and severity of AI errors and adverse events (AEs) in randomized controlled trials (RCTs) investigating AI medical devices as interventions in clinical settings. The review will also explore how performance errors are analyzed including whether the analysis includes the investigation of subgroup-level outcomes.
METHODS: This systematic review will identify and select RCTs assessing AI medical devices. Search strategies will be deployed in MEDLINE (Ovid), Embase (Ovid), Cochrane CENTRAL, and clinical trial registries to identify relevant papers. RCTs identified in bibliographic databases will be cross-referenced with clinical trial registries. The primary outcomes of interest are the frequency and severity of AI errors, patient harms, and reported AEs. Quality assessment of RCTs will be based on version 2 of the Cochrane risk-of-bias tool (RoB2). Data analysis will include a comparison of error rates and patient harms between study arms, and a meta-analysis of the rates of patient harm in control versus intervention arms will be conducted if appropriate.
RESULTS: The project was registered on PROSPERO in February 2023. Preliminary searches have been completed and the search strategy has been designed in consultation with an information specialist and methodologist. Title and abstract screening started in September 2023. Full-text screening is ongoing and data collection and analysis began in April 2024.
CONCLUSIONS: Evaluations of AI medical devices have shown promising results; however, reporting of studies has been variable. Detection, analysis, and reporting of performance errors and patient harms is vital to robustly assess the safety of AI medical devices in RCTs. Scoping searches have illustrated that the reporting of harms is variable, often with no mention of AEs. The findings of this systematic review will identify the frequency and severity of AI performance errors and patient harms and generate insights into how errors should be analyzed to account for both overall and subgroup performance.
BACKGROUND: PROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747.
UNASSIGNED: PRR1-10.2196/51614.

BACKGROUND: Metabolomics data is often complex due to the high number of metabolites, chemical diversity, and dependence on sample preparation. This makes it challenging to detect significant differences between factor levels and to obtain accurate and reliable data. To address these challenges, the use of Design of Experiments (DoE) techniques in the setup of metabolomic experiments is crucial. DoE techniques can be used to optimize the experimental design space, ensuring that the maximum amount of information is obtained from a limited sample space.
OBJECTIVE: This review aims at providing a baseline workflow for applying DoE when generating metabolomics data.
UNASSIGNED: The review provides insights into the theory of DoE. The review showcases the theory being put into practice by highlighting different examples DoE being applied in metabolomics throughout the literature, considering both targeted and untargeted metabolomic studies in which the data was acquired using both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry techniques. In addition, the review presents DoE concepts not currently being applied in metabolomics, highlighting these as potential future prospects.

暂无摘要。

BACKGROUND: Experience-Based Co-Design (EBCD) is a multi-stage participatory action research process which was developed originally to increase patient involvement in service improvement initiatives. This viewpoint article serves as a reflection on the researchers\' experiences, focusing on the application and feasibility of participatory approaches, particularly co-design, in the specific context of early-phase clinical trials.
METHODS: We reflect on the opportunities and challenges of applying EBCD in a new context of early-phase clinical trials in oncology where experimental treatments are increasingly perceived as a therapeutic option and, in certain instances, their efficacy may lead to accelerated approval facilitating a swifter integration into standard care.
RESULTS: We propose that the opportunity of applying EBCD in such trials lies in improving the delivery of person-centered care, care coordination, and support during the transition from experimental to standard care. Three potential challenges when applying EBCD in early-phase clinical trials are discussed related to: the need for standardization in trial processes; planning EBCD in a context of high uncertainty; and vulnerability of patient populations.
CONCLUSIONS: Integrating EBCD into early-phase oncology trials presents an opportunity to enhance person-centered care and can lead to simultaneous improvements in care processes and therapeutic development.
UNASSIGNED: This article has been developed with the collaboration of a patient partner who serves on the advisory board of our ongoing EBCD study in early clinical trials.

BACKGROUND: Randomized controlled trials (RCTs) are rigorous scientific research designs for evaluating intervention effectiveness. However, implementing RCTs in a real-world context is challenging. To develop strategies to improve its application, it is essential to understand the strengths and challenges of this design. This study thus aimed to explore the strengths, challenges, and strategies for improving the implementation of a pragmatic multicenter, prospective, two-arm RCT evaluating the effects of the Personalized Citizen Assistance for Social Participation (Accompagnement-citoyen Personnalisé d\'Intégration Communautaire: APIC; weekly 3-h personalized stimulation sessions given by a trained volunteer over a 12-month period) on older adults\' health, social participation, and life satisfaction.
METHODS: A multiple case study was conducted with 14 participants, comprising one research assistant, seven coordinators, and six managers of six community organizations serving older adults, who implemented the APIC in the context of a RCT. Between 2017 and 2023, qualitative data were extracted from 24 group meetings, seven semi-directed interviews, emails exchanged with the research team, and one follow-up document.
RESULTS: Aged between 30 and 60 (median ± SIQR: 44.0 ± 6.3), most participants were women from organizations already offering social participation interventions for older adults and working with the public sector. Reported strengths of this RCT were its relevance in assessing an innovative intervention to support healthy aging, and the sharing of common goals, expertise, and strategies with community organizations. Challenges included difficulties recruiting older adults, resistance to potential control group assignments, design complexity, and efforts to mobilize and engage volunteers. The COVID-19 pandemic lockdown and health measures exacerbated challenges related to recruiting older adults and mobilizing volunteers and complicated delivery of the intervention. The strategies that mostly overcame difficulties in recruiting older adults were reducing sample size, simplifying recruitment procedures, emphasizing the health follow-up, extending partnerships, and recognizing and supporting volunteers better. Because of the lockdown and physical distancing measures, the intervention was also adapted for remote delivery, including via telephone or videoconferencing.
CONCLUSIONS: Knowledge of the strengths and challenges of pragmatic RCTs can contribute to the development of strategies to facilitate implementation studies and better evaluate health and social participation interventions delivered under real-life conditions.
BACKGROUND: NCT03161860; Pre-results. Registered on May 22, 2017.

BACKGROUND: Evaluation publications typically summarize the results of studies to demonstrate the effectiveness of an intervention, but little is shared concerning any changes implemented during the study. We present a process evaluation protocol of a home-based gait, balance, and resistance exercise intervention to ameliorate persistent taxane-induced neuropathy study according to 7 key elements of process evaluation.
METHODS: The process evaluation is conducted parallel to the longitudinal, randomized control clinical trial examining the effects of the home-based gait, balance, and resistance exercise program for women with persistent peripheral neuropathy following treatment with taxanes for breast cancer (IRB approval: Pro00040035). The flowcharts clarify how the intervention should be implemented in comparable settings, fidelity procedures help to ensure the participants are comfortable and identify their individual needs, and the process evaluation allows for the individual attention tailoring and focus of the research to avoid protocol deviation.
CONCLUSIONS: The publication of the evaluation protocol plan adds transparency to the findings of clinical trials and favors process replication in future studies. The process evaluation enables the team to systematically register information and procedures applied during recruitment and factors that impact the implementation of the intervention, thereby allowing proactive approaches to prevent deviations from the protocol. When tracking an intervention continuously, positive or negative intervention effects are revealed early on in the study, giving valuable insight into inconsistent results. Furthermore, a process evaluation adds a participant-centered element to the research protocols, which allows a patient-centered approach to be applied to data collection.
BACKGROUND: ClinicalTrials.gov NCT04621721, November 9, 2020, registered prospectively.
METHODS: April 27, 2020, v2.

Recent publications within Contextual Behavioral Science provided a rationale for the expansion of intervention efficacy research using methods that capture idiographic factors and processes. We conducted a systematic review of the use and quality of single-case experimental designs (SCED) within the Acceptance and Commitment Therapy (ACT) literature in adult clinical populations. The systematic review was conducted according to PRISMA guidelines and the databases CINAHL, MEDLINE, PsycINFO, PsycArticles and OpenGrey were searched for peer-reviewed articles. Further studies were sought through review of reference lists of all full text studies. Studies were assessed against What Works Clearinghouse (WWC) single-case design standards. Twenty-six studies met eligibility criteria and were conducted within research teams all implementing multiple-baseline designs. Twenty-four studies did not meet WWC standards with most failing to ensure a degree of concurrence across participants. The extent of randomisation methods was also captured. The review highlights the sparsity of SCEDs within ACT literature in clinical populations and current methodological practices. Limitations of the review and implications for future research are discussed.

OBJECTIVE: Some large, randomized trials investigating red cell transfusion strategies have significant numbers of transfusions administered outside the trial study period. We sought to investigate the potential impact of this methodological issue.
METHODS: Meta-analysis of randomized controlled trials comparing liberal versus restrictive transfusion strategies in cardiac surgery and acute myocardial infarction patients. The outcome of interest was 30-day or in-hospital mortality.
RESULTS: In cardiac surgery, the pooled risk ratio for mortality was 0.83 (95% CI 0.62-1.12, P=0.22) times lower in the restrictive group when compared to the liberal group in trials applying a transfusion strategy throughout the patient\'s entire perioperative period, and 1.33 (95% CI 0.84-2.11, P=0.22) times higher in the restrictive group in trials not applying transfusion strategies throughout the entire perioperative period. When combined, the risk ratio for mortality was 0.98 (95% CI 0.73-1.32, P=0.89). In patients with acute myocardial infarction, the risk ratio for mortality was 0.72 (95%CI 0.40-1.28, P=0.26) times lower in the restrictive group when compared to the liberal group in one trial excluding patients administered the intervention pre-randomization and 1.19 (95% CI 0.96-1.47, P=0.11) times higher in the restrictive group in one trial including patients receiving the intervention pre-randomization. When combined the risk ratio for mortality was 1.00 (0.62-1.59, P=0.99).
CONCLUSIONS: Though not statistically significant, there was a consistent difference in trends between randomized controlled trials administering significant numbers of transfusion outside the trial study period compared to those that did not. The implications of our results may extend to randomized controlled trials in other settings that ignore if and how frequently an investigated therapy is administered outside the trial window.

Assuming that a hypothesis is true because insufficient evidence has been found to reject it is a very common error when interpreting the p-value of a test in biomedical research. For example, a value of p = 0.28 obviously does not mean the null hypothesis should be ruled out, but if we understand what it means (which is not a mathematical issue, but instead a purely logical one) that it is equally obvious that it cannot be stated that it is true. If the samples in a comparison of a new drug with an old one show that the new one has a higher healing percentage and the p-value of the test is 0.0004, for example, the scientific community concludes that the new one is better. However, if for example the p-value of the test is 0.14, the scientific community does not conclude that the new one is as good as the old one. It merely concludes that the new one has not been shown to outperform the other one. It is therefore possible that an extension of the study with more cases may demonstrate that the new one is better.
BACKGROUND: La abismal diferencia entre no rechazar la hipótesis nula y afirmar que es cierta.
Un error muy frecuente al interpretar el valor de p del test en la investigación biomédica consiste en asumir que una hipótesis es cierta porque no se ha encontrado suficiente evidencia para rechazarla. Es obvio que un valor de p = 0,28, por ejemplo, no invita a rechazar la hipótesis nula, pero, si se entiende lo que indica (que no es un tema matemático, sino puramente lógico), resulta igualmente obvio que no autoriza a afirmar que es cierta. Si al comparar un nuevo fármaco frente al antiguo encontramos en las muestras que el nuevo presenta mayor porcentaje de curaciones y, por ejemplo, el valor de p del test es = 0,0004, la comunidad científica concluye que el nuevo es mejor. Pero si, por ejemplo, el valor de p del test es = 0,14, la comunidad científica no concluye que el nuevo es igual al antiguo. Sólo concluye que no queda demostrado que el nuevo aventaje al otro. Por ello, queda abierta la puerta a que en una ampliación del estudio con más casos pueda demostrarse que el nuevo es mejor.