Intervention Tool Research Articles

Challenges of Interference and Disinformation in Italy: Countering New Threats to National Security

The focus of this article is the phenomenon of disinformation and foreign interference in Italy. The aim of the study is to examine Italy's approach to countering interference and combating disinformation. The article presents a scientific discussion on the terminology of the concept of “interference” and demonstrates that the lack of a definition does not detract from the results obtained on the topic. In countering interference in the broadest sense, as a threat to national security and the constitutional order of the country, Italy relies primarily on the national resources, where the main body is a special structure - the System for the Security of the Republic. The Special Parliamentary Committee for the Security of the Republic plays an important role as well. The issue of disinformation as an intervention tool has received more attention from researchers and institutions. The EU clearly distinguishes between “disinformation” and “misinformation”. In 2023, Italy was the first EU country to spread disinformation (in terms of the amount of deleted content from social media). In combating disinformation, Italy relies on the EU's guidelines and experience, transferring content moderation responsibilities to the EU level. The EU reacts with harsh methods, resorting to content removal and blocking of resources. Thus, the more Italy delegates its information security to the European Union, the more Rome involves in the information confrontation and war of narratives between Brussels and Moscow.

Stating Rules or Imparting Knowledge? An Intervention Experiment on the Trust and Sharing of Seismic Rumors in the Beijing-Tianjin-Hebei Area of China

AbstractSeismic rumors can mislead the public and trigger unnecessary actions, underscoring the importance of their control in disaster management. This study examined the impact of two different intervention tools—rule-based intervention and knowledge-based intervention—on the trust and sharing of seismic rumors. We designed a survey experiment to explore this issue, and 500 respondents participated in the experiment. The results indicate that the rule-based intervention significantly reduced the public’s trust in and intention to share seismic rumors, but the knowledge-based intervention failed. Possible mechanisms are that the rule-based intervention raises awareness of the unreliability of disaster information sources and costs associated with sharing rumors. It is suggested that communicating the existing rules and policies regarding disaster information release might be an effective approach to rendering disaster rumors uncreditable and then reducing people’s intention to share. These findings enrich our understanding of the effectiveness of different intervention tools regarding rumor behavior in disaster scenarios and offer insights for practical seismic rumor management.

The effect of a 2-month abacus training on students with developmental dyscalculia.

Developmental dyscalculia (DD) is a specific mathematics learning disorder, characterized by the atypical development of number sense, arithmetic calculation, and atypical development of brain structures and brain activations in core brain regions for number processing. The current study examined the intervention effect of a 2-month abacus training on DD students. Results showed that compared with the non-trained control group, the DD students with abacus training showed higher scores in number sense, calculation, and sustained attention abilities. Additionally, a larger percentage of students in the abacus group showed improvements in the DD screening tasks compared to the control group. The current finding indicated that abacus training or abacus courses can be used as a tool for further DD intervention.

Promoting Physical Activity in the Management of Type 2 Diabetes: A Feasibility Study to Develop Intervention Tools for Delivery of Diabetes‐Specific Education

ABSTRACTBackgroundPhysical activity is an important aspect of lifestyle management and type 2 diabetes, although the percentage of people with type 2 diabetes achieving recommended guidelines is low. Supported self‐management underpinned by group educational programmes may be helpful but difficult to implement in remote and rural areas. We aimed to test the feasibility of an approach based on education delivered individually by community‐based exercise advisors to people with type 2 diabetes.MethodsFollowing the development of an online educational toolkit a mixture of exercise advisors and people with type 2 diabetes were recruited. People with diabetes had a face‐to‐face consultation with an exercise advisor with mutually agreed follow‐up over 6 months. To track physical activity, people with diabetes aimed to wear an accelerometer device for 7 days at baseline, 3 months and 6 months. Post‐intervention semi‐structured interviews were undertaken with both groups of participants to gauge perspectives of the initiative.ResultsThere was a 56% total attrition rate from baseline to 3 months due to COVID‐19 and its impact on clinical research. Around 50% of participants achieved minimum physical activity recommendations at each time point and 22% of participants had accelerometer data at 3 time points. People with diabetes valued interaction with exercise advisors and felt that the programme would be of greatest benefit to less active individuals. Exercise advisors felt that the programme provided more opportunities and increased confidence and that training in working with older less active individuals would be useful for them.ConclusionIt is feasible to develop a physical activity programme delivered by non‐healthcare practitioners underpinned by diabetes‐specific education tailored to people with type 2 diabetes. Several project adaptions should be considered for progress to a pilot study to assess an integrated physical activity programme delivered by community exercise advisors.

Construction of a back propagation neural network model for predicting urosepsis after flexible ureteroscopic lithotripsy based on heparin-binding protein and C-reactive protein levels.

To analyze the association of serum heparin-binding protein (HBP) and C-reactive protein (CRP) levels with urosepsis following flexible ureteroscopic lithotripsy (FURL) and to construct a back propagation neural network prediction model. A total of 428 patients with kidney stones who underwent FURL were enrolled. Patients were divided into sepsis group (n=42) and control group (n=386) according to whether post-operative urosepsis developed. Logistic regression analysis was used to determine the risk factors and interactions of post-FURL urosepsis. A Logistic regression model and a neural network model were developed for predicting post-FURL urosepsis following FURL, and their predictive performance was evaluated using ROC curves. Univariate analysis showed that stone surgery history, gender, urine culture, stone diameter, diabetes, operation time, white blood cell (WBC), platelet, CRP, and HBP levels were significantly associated with post-FURL urosepsis (P<0.05). Multivariate analysis identified positive urine culture, CRP, and HBP levels as independent risk factors for post-FURL urosepsis (P<0.05). Interaction analysis revealed that CRP and HBP showed both additive (RERI=8.453, 95%CI: 2.645-16.282; AP=0.696, 95%CI: 0.131-1.273; S=3.369, 95%CI: 1.176-7.632) and multiplicative (OR=1.754, 95%CI: 1.218-3.650) interactions, while CRP and urine culture demonstrated multiplicative interaction (OR=2.449, 95%CI: 1.525-3.825). The neural network model showed superior predictive performance compared to the Logistic regression model. CRP and HBP levels are independent risk factors for post-FURL urosepsis. The neural network model based on CRP and HBP exhibits higher predictive accuracy than the Logistic regression model, which may provide a reliable risk assessment tool for early discrimination and intervention of post-FURL urosepsis.

Development of Immersive 360° Virtual Reality Videos for Intervention with Test Anxiety

This study explores the impact of 360-degree virtual reality (VR) videos on simulating test anxiety in students who already experience it. Using a phenomenological design, participants answered questions about their immersion in the virtual environment, including the realism of visuals, emotional responses, and adaptation. Seven students (four girls and three boys) were selected based on specific criteria, and the collected data underwent content analysis. Findings indicate that 360-degree VR videos effectively simulated test environments, creating a strong sense of realism and immersion. Participants reported that VR exposure elicited test anxiety symptoms similar to those experienced in real tests, with emotional distress intensifying during VR sessions. Test-anxious individuals noted that VR exposure triggered stronger emotional responses compared to imagination-based scenarios. While VR exposure proved effective in evoking realistic experiences and emotions, it was not deemed superior to real-life exposure techniques. This suggests that 360-degree VR has potential as a tool for anxiety interventions.

Technological Due Process: Review, Optimization, and Prospects

The Houston Independent School District case has revealed the challenges of transparency, impartiality, and human review mechanisms that automated administrative systems face in practice. The case shows that the automated administrative system needs to solve the problems of insufficient transparency and impartiality challenges while enhancing efficiency. At the theoretical level, the connotation of technical due process is further enriched in order to effectively respond to the complex and realistic needs. At the practical level, automated administrative systems with scenarios are constantly applied. In the face of the generalization of technical due process, we need to be alert to potential technical bugs and legal risks. In order to ensure the healthy development of the automated administrative system, it is necessary to pay attention to system updating and continuous optimization in the future, and introduce new technologies to improve the performance. At the same time, establish a sound error correction and human intervention mechanism to ensure that the automated system can be corrected quickly in the event of an error, and to strengthen the timeliness and effectiveness of human intervention. In addition, improving the transparency and fairness of the system, and perfecting manual intervention and decision support tools are also important directions for future development. The research will provide a reference for the optimization and improvement of automated administrative systems.

ClassPoint to Classroom Learning Enhancement: An Interventional Tool Integrated in PowerPoint Presentations

This research investigated the effectiveness of interactive ClassPoint presentations in enhancing students’ engagement and understanding of Science concepts in Grade 8. A quasi-experimental design with a quantitative follow-up survey was employed in this study. The participants were the 50 Grade 8 students who were divided into control and experimental groups, each comprising 25 learners. The control group was exposed to regularly-used teaching modalities, including PowerPoint presentations. Meanwhile, the experimental group was employed with ClassPoint presentations. A pre-test/post-test was used to measure effectiveness, and a survey questionnaire was used to gather students’ feedback on ClassPoint. The gathered data was analysed using the Wilcoxon signed-rank test, Mann-Whitney U test, and Frequency distribution. Findings revealed that the utilisation of ClassPoint presentations has significantly improved the scores of the students and is a better teaching modality when compared to the other learning modalities used. In addition, over half of the students strongly agreed that ClassPoint has increased their motivation (64%) and their engagement in the class (56%). This feedback aligned with their strong agreement that lessons were lively and interesting (64%), andClassPoint’s display slides helped them to focus and follow the teacher’s presentation better (68%). Nearly half (48%) also found these activities helpful for self-reflection on their learning progress. These findings suggested that ClassPoint can be employed as an alternative learning strategy for Science 8.

Harnessing adapted capsule networks for accurate lumpy skin disease diagnosis in cattle

Lumpy skin disease (LSD) poses a substantial risk to livestock, emphasizing the critical need for reliable diagnostic tools to ensure timely intervention. The considerable economic impact of LSD further accentuates the imperative for efficient diagnosis. In this context, artificial intelligence (AI) emerges as a transformative solution, playing a pivotal role in providing swift detection capabilities. Rapid identification of LSD not only alleviates economic burdens but also impedes the disease’s propagation with in herds. A ground breaking in iterative involves the implementation of an adapted capsule network (CapsNet) expressly designed for diagnosing LSD. This innovative strategy is finely tuned to discern intricate patterns in disease manifestation, achieving an impressive accuracy rateof 97.6%. The model’s effectiveness is evident in it is capacity to differentiate between infected and healthy cases, with precision, recall, and F1 score metrics registering at 9.65%, 97.1%, and 96.3%, respectively. This high level of precision underscores the model’s proficiency in minimizing errors, solidifying its role as a dependable tool for precise LSD diagnosis and intervention, contributing significantly to the overall health and economic well-being of livestock populations.

The epidemiological benefit of pyrethroid–pyrrole insecticide treated nets against malaria: an individual-based malaria transmission dynamics modelling study

Insecticide treated nets (ITNs) are the most important malaria prevention tool in Africa but the rise of pyrethroid resistance in mosquitoes is likely impeding control. WHO has recommended a novel pyrethroid-pyrrole ITN following evidence of epidemiological benefit in two cluster-randomised, controlled trials (CRTs). It remains unclear how effective more costly pyrethroid-pyrrole ITNs are compared with other tools, or whether they should be deployed when budgets are limited. We aimed to compare the epidemiological impact and cost-effectiveness of the mass distribution of pyrethroid-pyrrole ITNs relative to other ITNs over 3 years in different African settings. In this individual-based malaria transmission dynamics modelling study we characterise the entomological impact of ITNs using data from a systematic review of experimental hut trials from across Africa. This African entomological data was used to inform an individual-based malaria transmission dynamics model, which was validated against CRT results from Benin and Tanzania. The full impact of new ITNs was quantified for trial sites and simulation was used to project impact in different settings which were included within an accessible interface (the Malaria Intervention Tool) to support National Malaria Programmes to explore how vector control tools and budgets could be allocated across regions to avert the most cases. The model projects that distributing pyrethroid-pyrrole ITNs averted 65% (95% credible interval 48-74) of cases over 3 years in Tanzania, and 75% (28-93) in Benin. The model indicates that trials might have underestimated the benefits of switching ITNs by 12-16% over 3 years because participants stopped using trial-allocated nets. In moderate endemicity non-trial settings, pyrethroid-pyrrole ITNs are projected to reduce malaria prevalence by 25-60% and switching from pyrethroid-only ITNs is probably highly cost-effective in most locations given current prices, averting an additional 10-30% of cases. The benefit of pyrethroid-pyrrole ITNs varies by setting but is generally the most cost-effective indoor vector control intervention in Africa. National Malaria Programmes can strategise deployment to maximise impact. Entomological data could broadly predict epidemiological impact, although there are some inconsistencies, illustrating the challenge in capturing the dynamics across diverse settings. Unitaid, Bill & Melinda Gates Foundation, the UK Medical Research Council, Wellcome Trust, and the UK Foreign, Commonwealth & Development Office.

Understanding risk factors of post-stroke mortality

Stroke is one of the leading causes of death worldwide. Understanding the risk factors for post-stroke mortality is crucial for improving patient outcomes. This study analyzes and predicts post-stroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several Machine Learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity as the most critical. These findings provide healthcare professionals with valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions.

Perceptions and Needs for a Technology-Based Dyadic Intervention on Symptom Management Among Patients With Colorectal Cancer and Their Caregivers: A Qualitative Study.

Colorectal cancer (CRC) patients and their caregivers often experience multiple co-occurring symptoms (eg, fatigue, depression, anxiety, and sleep disturbance). There is a noticeable gap in research regarding symptom management for patient-caregiver dyads, particularly using technology-based tools. This study aimed to describe the needs and perceptions of patient-caregiver dyads regarding a technology-based tool to manage their multiple symptoms. This study utilized a qualitative descriptive design with purposive sampling. Semistructured interviews were conducted with CRC patients undergoing chemotherapy and their caregivers. Thematic analysis was performed to identify key themes. The study included 11 patients and 8 caregivers (8 patient-caregiver dyads and 3 patients without dyads). Four main themes emerged: (1) high symptom burden experienced by CRC patients and their caregivers; (2) symptom self-management strategies, including information seeking and self-education, family and social support, and coping strategies; (3) input and suggestions for technology use, covering aspects such as content, format, and specific technology features; and (4) concerns about barriers to healthcare access, including technological literacy, geographical distance, and time constraints. Our findings provide novel insights into experiences, needs, and suggestions of CRC patient-caregiver dyads for managing multiple symptoms. Participants emphasized the importance of developing a tailored, technology-based tool that includes personalized, evidence-based content; accessible and intuitive design features; and interactive social support capabilities. Leveraging these findings, we aim to develop a technology-based dyadic intervention tool that addresses the psychosocial needs of CRC patient-caregiver dyads. This approach can also enhance health equity by making care more accessible.

Improving the Health and Environmental Health Literacy of Professionals: Evaluating the Effect of a Virtual Intervention on Phthalate Environmental Health Literacy

The American College of Obstetricians and Gynecologists provided updated guidance in 2021, recommending that reproductive health professionals should include discussion of environmental exposures with their patients. However, environmental health is seldom included in medical training, with endocrine-disrupting chemicals, such as phthalates—linked to adverse pregnancy outcomes—being among the least discussed. We developed a one-hour virtual educational intervention to train reproductive health professionals on the routes of phthalate exposure, potential associated health impacts, and suggestions on how to discuss exposure reduction with patients. The intervention was designed to include perspectives from patients, scientists, and clinicians. Using a pre/post/post design, we evaluated the impact of the intervention on reproductive health professionals’ phthalate-related reproductive health literacy via a validated environmental health literacy (EHL) scale, their confidence in discussing phthalates, and the frequency of discussions about phthalates with patients. All materials, including the study questionnaires and intervention materials, were administered virtually to reproductive health professionals (n = 203) currently seeing patients working in the United States. After completing the intervention, reproductive health professionals’ average EHL increased (pre-course: 22.3, post-course: 23.7, 2 months post-course: 24.0), as did their confidence in discussing phthalates with their patients (pre-course: 1% (2/203) reported being quite confident, post-course: 64% (131/203) reported being quite confident, and 2 months post course: 86% (174/203) reported being quite confident). Additionally, the reported frequency of discussions about phthalates with patients rose substantially (pre-course: 0% (0/203) reported usually discussing phthalates with patients, and 2 months post-course: 86% (175/203) reported usually discussing phthalates with patients): In line with the recommendations of the American College of Obstetricians and Gynecologists, this online phthalate educational intervention tool increased EHL among reproductive health professionals and shifted clinical care to include discussion about phthalates, a reproductive toxicant.

An Ecological Momentary Assessment and Intervention Tool for Memory in Chronic Traumatic Brain Injury: Development and Usability of Memory Ecological Momentary Intervention.

Memory and learning deficits are among the most impactful and longest-lasting symptoms experienced by people with chronic traumatic brain injury (TBI). Despite the persistence of post-TBI memory deficits and their implications for community reintegration, memory rehabilitation is restricted to short-term care within structured therapy sessions. Technology shows promise to extend memory rehabilitation into daily life and to increase the number and contextual diversity of learning opportunities. Ecological momentary assessment and intervention frameworks leverage mobile phone technology to assess and support individuals' behaviors across contexts and have shown benefits in other chronic conditions. However, few studies have used regular outreach via text messaging for adults with chronic TBI, and none have done so to assess and support memory. This study aimed to develop and test the usability of memory ecological momentary intervention (MEMI), a text message-based assessment and intervention tool for memory in daily life. MEMI is designed to introduce new information, cue retrieval of the information, and assess learning across time and contexts. We tested MEMI via an iterative, user-centered design process to ready it for a future trial. We developed MEMI by leveraging automated text messages for prompts using a REDCap (Research Electronic Data Capture)/Twilio interface linking to the Gorilla web-based behavioral experimental platform. We recruited 14 adults with chronic, moderate-severe TBI from the Vanderbilt Brain Injury Patient Registry to participate in 3 rounds of usability testing: one round of ThinkAloud sessions using the platform and providing real-time feedback to an experimenter (n=4) and 2 rounds of real-world usability testing in which participants used MEMI in their daily lives for a week and provided feedback (n=5/round). We analyzed engagement and quantitative and qualitative user feedback to assess MEMI's usability and acceptability. Participants were highly engaged with MEMI, completing an average of 11.8 out of 12 (98%) possible sessions. They rated MEMI as highly usable, with scores on the System Usability Scale across all rounds equivalent to an A+ on a standardized scale. In semistructured interviews, they stated that MEMI was simple and easy to use, that daily retrieval sessions were not burdensome, and that they perceived MEMI as helpful for memory. We identified a few small issues (eg, instruction wording) and made improvements between usability testing rounds. Testing MEMI with adults with chronic TBI revealed that this technology is highly usable and favorably rated for this population. We incorporated feedback regarding users' preferences and plan to test the efficacy of this tool in a future clinical trial.

Fluralaner treatment of chickens kills the southern house mosquito, Culex quinquefasciatus.

The control of zoonotic and vector-borne pathogens is challenging due to the limited availability of intervention tools. West Nile virus (WNV) is an example of a globally distributed zoonotic arbovirus that circulates between Culex species (Diptera: Culicidae) mosquitoes and avian hosts, with spillover transmission to humans, resulting in disease cases. Interventions delivering systemic insecticides to vertebrate hosts used by vector species, known as xenointoxication, are potential tools for managing vector populations by creating toxic bloodmeals. In this study, we evaluated the impact of two systemic pesticides (ivermectin; Ivomec® Pour-On and fluralaner; Bravecto®), and one anthelmintic (fenbendazole; Safe-Guard® Aquasol) on the mortality of Cx. quinquefasciatus Say (Diptera: Culicidae). We found no significant difference in the feeding rates of mosquitoes that fed on treated chickens compared with those fed on untreated chickens, suggesting that the treatment did not repel mosquitoes. The mortality of Cx. quinquefasciatus mosquitoes feeding on fluralaner-treated chickens was significantly higher (p < 0.01) than those fed on control chickens at 3 and 7 days post-treatment, but this effect was not observed in mosquitoes fed on chickens treated with fenbendazole or ivermectin. No differences in mortality were observed among the groups at 14, 26 or 56 days post-treatment. These data support fluralaner as a xenointoxication tool to control Cx. quinquefasciatus populations and decrease the risk of human exposure to their associated pathogens.

Enhanced dual-mode imaging: Superior photoacoustic and ultrasound endoscopy in live pigs using a transparent ultrasound transducer.

Dual-mode photoacoustic/ultrasound endoscopy (ePAUS) is a promising tool for preclinical and clinical interventions. To be clinically useful, ePAUS must deliver high-performance ultrasound imaging comparable to commercial systems and maintain high photoacoustic imaging performance at long working distances. This requires a transducer with an intact physical aperture and coaxial alignment of acoustic and optical beams within the probe, a challenging integration task. We present a high-performance ePAUS probe with a miniaturized, optically transparent ultrasonic transducer (TUT) called ePAUS-TUT. The 1.8-mm-diameter probe, fitting into standard endoscopic channels, aligns acoustic and optical beams efficiently, achieving commercial-level ultrasound and high-resolution photoacoustic imaging over long distances. These imaging capabilities were validated through in vivo imaging of a rat's rectum and a pig's esophagus. The ePAUS-TUT system substantially enhances feasibility and potential for clinical applications.

Associations Between Physical Activity and Symptoms of Mental Health Disorders in Adolescence: Evidence From the Longitudinal Swedish Twin Register

PurposeThe transition phase between childhood and adolescence is critical, given the observation of increased mental health issues during this period, coupled with the absence of efficient intervention tools. This study aims to examine a possible effect of physical activity on mental health symptoms in adolescents during this transitional age. MethodsWe used data from a large-scale population-based Swedish twin sample of 14,083 individuals to investigate cross-sectional and longitudinal associations between physical activity and mental health problems during childhood and adolescence. We examine different aspects of mental health symptoms, including internalizing and externalizing symptoms. We applied generalized estimating equations to investigate the associations and explored potential familial confounding using within-twin pair analysis. ResultsWe found high physical activity intensity and frequency to be associated with better mental health throughout childhood and adolescence, cross-sectionally and longitudinally. Compared to those who reported low, those reporting high physical activity intensity and frequency in both childhood and/or adolescence had significantly lower symptoms of mental health at age 15. This association was statistically significant for both internalizing and externalizing symptoms. The within-twin pair analysis showed that familial confounding factors can largely explain the association with the externalizing scale, but not the internalizing scale. DiscussionPhysical activity in childhood and adolescence may play an essential role in reducing the risk of developing mental health problems, especially internalizing symptoms. Implementing acceptable, low-risk, and cost-effective interventions to promote physical activity could effectively safeguard young individuals from these symptoms.

Systematic review to understand users perspectives on AI-enabled decision aids to inform shared decision making

Artificial intelligence (AI)-enabled decision aids can contribute to the shared decision-making process between patients and clinicians through personalised recommendations. This systematic review aims to understand users’ perceptions on using AI-enabled decision aids to inform shared decision-making. Four databases were searched. The population, intervention, comparison, outcomes and study design tool was used to formulate eligibility criteria. Titles, abstracts and full texts were independently screened and PRISMA guidelines followed. A narrative synthesis was conducted. Twenty-six articles were included, with AI-enabled decision aids used for screening and prevention, prognosis, and treatment. Patients found the AI-enabled decision aids easy to understand and user-friendly, fostering a sense of ownership and promoting better adherence to recommended treatment. Clinicians expressed concerns about how up-to-date the information was and the potential for over- or under-treatment. Despite users’ positive perceptions, they also acknowledged certain challenges relating to the usage and risk of bias that would need to be addressed.Registration: PROSPERO database: (CRD42020220320)

The implementation of preoperative optimization in British Columbia: a quality improvement initiative.

Surgical patient optimization is a proactive approach to improve postoperative outcomes. This article reviews the development of the Surgical Patient Optimization Collaborative, an initiative supporting preoperative optimization in British Columbia, Canada. The Collaborative facilitated optimization programs over two cohorts between 2019 and 2024. A "Change Package" offered screening, intervention, and measurement tools for the following 13 surgical risk factors: anemia, anxiety, body mass index (cohort 2 only), cardiac status, frailty, glycemic control, nutrition, sleep apnea, pain management, physical activity, smoking, social support, substance use, and venous thromboembolism (cohort 1 only). Monthly data submissions from participating sites included the number of patients undergoing optimization, National Surgical Quality Improvement Program-defined 30-day outcomes, length of stay, and patient-reported measures. Run charts were used to analyze the progress of optimization implementation across both cohorts. Fourteen sites participated in each cohort. In total, 9,686 patients were screened for optimization, with 7,100/7,505 (95%) patients receiving at least one optimization intervention. Improvement shifts in the number of patients screened were identified in the run charts across both cohorts. Most patients felt that their optimization improved their surgical experience and outcomes. Data for clinical outcomes were inconsistently reported from sites and precluded analyses. Barriers to implementation included project complexity and structural characteristics, and facilitators were knowledge and beliefs about the intervention, reflection, and evaluation. Preoperative optimization programs were successfully implemented across multiple sites in British Columbia. High-quality clinical outcome analyses are still needed to determine the impact of preoperative optimization on postoperative outcomes. The insight gained from the Collaborative's implementation process may help inform future multicentre preoperative optimization efforts.

Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques

Lung cancer remains a leading cause of cancer-related deaths globally. Early and accurate detection is crucial for improving patient outcomes. Traditional methods, relying on manual interpretation of medical images, are time-consuming and prone to errors. Deep learning, particularly convolutional neural networks (CNNs), offers an automated alternative capable of learning intricate patterns from medical images. However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. This paper presents a novel deep learning methodology that addresses these limitations. Our approach leverages expansive datasets, incorporates advanced feature extraction techniques, improves interpretability, and accommodates the diverse nature of lung cancer. Specifically, we develop dedicated models for both chest X-ray and CT images utilizing publicly available datasets from Kaggle. Through the integration of feature selection and model selection techniques—such as employing a genetic algorithm in conjunction with the tree-based pipeline optimization tool (TPOT)—we achieved remarkable accuracy. Our X-ray model attains an overall accuracy of 95.47%, the CT model achieves an accuracy of 98.70%, and the combined model achieves an impressive overall accuracy of 98.93%. Our methodology significantly enhances the performance and efficiency of lung cancer detection and is a valuable tool for early diagnosis and intervention.