Level Of Agreement Research Articles

Level of Agreement between Emotions Generated by Artificial Intelligence and Human Evaluation: A Methodological Proposal

Images are capable of conveying emotions, but emotional experience is highly subjective. Advances in artificial intelligence have enabled the generation of images based on emotional descriptions. However, the level of agreement between the generative images and human emotional responses has not yet been evaluated. In order to address this, 20 artistic landscapes were generated using StyleGAN2-ADA. Four variants evoking positive emotions (contentment and amusement) and negative emotions (fear and sadness) were created for each image, resulting in 80 pictures. An online questionnaire was designed using this material, in which 61 observers classified the generated images. Statistical analyses were performed on the collected data to determine the level of agreement among participants between the observers’ responses and the generated emotions by AI. A generally good level of agreement was found, with better results for negative emotions. However, the study confirms the subjectivity inherent in emotional evaluation.

Feasibility of trauma video review for the collection of medication-related information for research and quality improvement.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Trauma video review (TVR) involves reviewing video recordings of team-based trauma response to evaluate team performance, identify improvement opportunities, and assess procedures, but the feasibility of using TVR to gather medication-related information is unknown. We aimed to assess the feasibility of using TVR for data collection of medication-related variables for research and quality improvement by comparing and describing differences between TVR and electronic medical record (EMR) review. This was an observational study of level I/II trauma patients treated between November 2022 and March 2023. Patients with video recording started within 1 minute of arrival, at least 1 medication administered, and with pharmacist participation in care were included. The number of variables able to be collected by TVR or EMR review were compared and reported in the categories of medication administration, indicators of adverse drug events (ADEs), medication errors, and communication. The numbers and types of discrepancies between data collection modalities were quantified and described. Agreement between TVR and EMR review was assessed and reported as an intraclass correlation coefficient (ICC). Twenty-five patients were included; 758 and 1,011 variables collected by TVR and EMR review, respectively. In total, 689 variables were collected by both methods, and data collection modalities matched exactly in 4 of 25 patients (16%); ICC, 0.677 (moderate level of agreement). There were 46 (6.7%) discrepancies; 84% involved communication related variables. TVR missed more variables than EMR review, mostly medication errors and inability to assess ADEs but captured more communication-related variables. TVR and EMR review together offer a greater source of medication-related information for data collection compared to either alone. EMR review collected medication administration, ADE, and medication errors variables more often than TVR and TVR was better able to collect communication-related variables. When designing studies/quality improvement efforts related to medication use during trauma resuscitation (e.g., pharmacist impact on time to administration), combined data collection modalities should be used, when available.

Validation of a data logger for measuring standing time and seat angles for community-based standing devices

Context Little is understood about community-based standing device use and the impact of standing on health outcomes (e.g. pressure injury) in those living with spinal cord injury (SCI). This project reports on the accuracy of a commercially available data logger for measuring standing time and seat angle. Methods A standing frame and a standing manual wheelchair were each instrumented with a commercially available data logger and each was tested by an non-disabled participant. Standing time in the standing frame was calculated from the data logger and compared to a user-recorded standing log over a two-month period in a laboratory environment. The standing wheelchair’s seat angle was calculated using motion capture and compared to the calculated seat angle from the data logger. Average seat interface pressures were also captured during the testing of the standing wheelchair. Results The data logger demonstrated high accuracy (99.99999%) in classifying sitting and standing in the standing frame when compared to a user-recorded standing log. The wheelchair seat angle calculated from the data logger demonstrated a high level of agreement with the motion lab calculations of seat angle (ICC = 0.96 (0.95, 0.97)). The data logger seat angle results also demonstrated strong relationships to average seat pressure and rear dispersion index, measures relevant to pressure injuries. Conclusions The data logger appears to be an appropriate tool for determining standing time and seat angle in standing devices, which may aid clinicians and researchers to better understand the use and impact of standing technologies on health outcomes.

Evaluation of Two Commercial ELISA Kits for Measuring Equine Serum Gastrin Compared to Radioimmunoassay.

Gastrin is an important hormone involved in gastric acid secretion. Despite its importance, validated methods other than radioimmunoassay (RIA) to assess serum gastrin concentrations in horses are lacking. This study aims to determine the agreement between ELISA and RIA in quantifying equine serum gastrin concentrations. Serum gastrin concentrations were quantified using two ELISA kits and RIA. Samples (196) from 14 horses at different time points were analyzed using one ELISA kit and RIA, selected samples (7) were analyzed using a second ELISA kit, and the correlation between methods was calculated. The level of agreement was analyzed by Bland-Altman analysis and differences between ELISA and RIA were plotted against averages for each sample. The Pearson correlation between gastrin concentrations measured by ELISA and the RIA was 0.27 and -0.32 for ELISA kit 1 and kit 2, respectively. Mean bias (ELISA-RIA) was 198.40 pg/mL (95% CI: -142.95-539.76) and -17.90 pg/mL (95% CI: -89.98-54.19) for ELISA kit 1 and kit 2, respectively. Measurements of horse gastrin by both ELISA methods were highly variable, with an unacceptable correlation to the reference method, RIA. Using non-validated ELISA methods to quantify horse gastrin cannot be recommended.

Beyond the Surface: Investigating Clinical and Magnetic Resonance Imaging Dynamics in Knee Injuries: A Cross-sectional Study

Abstract Context: Orthopedic surgeons frequently encounter knee soft tissue injuries such as meniscal and ligament injuries. This study seeks to explore the diagnostic efficacy of clinical examination, magnetic resonance imaging (MRI), and arthroscopy in identifying knee injuries. Subjects and Methods: This prospective observational study enrolled 120 patients with knee injuries slated for arthroscopic surgery based on clinical and MRI assessments. The study assessed and compared the diagnostic accuracy of clinical and MRI findings. Statistical Analysis Used: Statistical analysis was performed using SPSS software (SPSS Inc., Chicago, IL, USA) for the Windows program (21.0 version). The Spearman correlation analysis was done to assess the relationship between the various parameters. The level of agreement was assessed using Cohen’s kappa statistics. Interpretation of kappa statistics is as follows: 0 to 0.20-Slight agreement; 0.1 to 0.39-Fair; 0.40 to 0.59-Moderate; 0.60 to 0.79- Substantial and 0.80 to 0.90-Almost perfect agreement. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated and observed by using 2 × 2 contingency table. P < 0.05 was considered statistically significant. Results: When comparing MRI and arthroscopy findings, the anterior cruciate ligament (ACL) tear exhibited the highest sensitivity and specificity at 100.00% and 91.84%, respectively, while the medial meniscus tear had the lowest sensitivity and specificity at 80.23% and 61.76%. Regarding the comparison between clinical examination and arthroscopy, the Lachman test displayed the highest sensitivity at 95.92%, whereas McMurray’s test (Lateral Meniscus) had the lowest sensitivity at 54.55%, and the posterior Drawer’s test showed the lowest specificity at 60.00%. Lachman also demonstrated the highest diagnostic accuracy at 98.33%, while the posterior Drawer’s test had the lowest diagnostic accuracy at 70.00%. Conclusions: Despite a few limitations, clinical examination and MRI currently stand out as the most precise non-invasive methods for detecting abnormalities in the menisci and the ACL. These diagnostic approaches provide valuable insights into the structural integrity of these knee components, allowing for a comprehensive assessment without the need for invasive procedures.

The Impact of Academic Qualifications and Professional Training on Procurement Staff Performance in Tanzania: A Case Study of Morogoro Referral Hospital

This study investigates the crucial role of academic qualifications and professional training in enhancing procurement staff performance at Morogoro Referral Hospital, focusing on their impact across various procurement activities. The study utilizes a mixed research approach and a case study research design. The target population of the study comprised of individuals who are involved in procurement activities at Morogoro Referral Hospital. The sample size was 67 respondents selected through convenience and purposive sampling techniques. Data were collected by using questionnaire and interviews. Quantitative data were analyzed using descriptive statistics, while qualitative data is examined through thematic analysis. The study highlights the essential role of academic qualifications and professional training in boosting procurement staff performance, noting broad agreement on their benefits for foundational knowledge and critical skills such as supply chain management and negotiation. Despite varying levels of agreement on areas like supplier evaluation and risk mitigation, the overall positive impact is clear. It is recommended that organizations invest in targeted training programs to address performance gaps, particularly in risk mitigation and supplier evaluation, and ensure ongoing updates to keep staff current with regulations, technology, and best practices. This approach will enhance procurement efficiency and effectiveness, supporting organizational success.

Erdosteine in the treatment of acute and chronic diseases of the respiratory system: resolution of the scientific forum of experts “In the Spotlight”

Airway mucus hypersecretion is a pathophysiologic manifestation of acute and chronic airway inflammatory disease. Mucolytics can reduce mucus viscosity and promote mucus discharge and therefore can be considered pathogenetically based therapy.The purpose of this publication with the resolution of the scientific forum of experts was to discuss pharmacological features, efficacy, and safety of erdosteine.Results. Experts of thescientific forum not only reviewed the results of clinical trials, but also voted on the use of mucoactive drugs according to the principle of Delphi consensus. Comparison of the efficacy and safety of mucoactive drugs (erdosteine, acetylcysteine, carbocysteine, ambroxol) in patients with respiratory diseases by the experts during voting showed that the highest level of agreement among the experts was achieved for the drug Elmucin® (erdosteine) in terms of its mucolytic activity and other pharmacological features, as well as when discussing the safety of the compared drugs in the officially recommended doses. The highest level of agreement was also noted on the erdosteine efficacy in COPD patients in reducing the frequency and duration of disease exacerbations, as well as reducing the risk of hospitalization of COPD patients when used long-term as part of combination therapy. The demonstrated safety profile was the most beneficial among the reviewed mucoactive drugs.Conclusion. Elmucin® is a mucolytic with pleiotropic effects such as complex mucoactive action, potent antioxidant, anti-inflammatory and antibacterial activity with anti-adhesive effect for pathogens that determine its clinical efficacy in the treatment of acute and chronic airway diseases.

Patients with low prognosis in ART: a Delphi consensus to identify potential clinical implications and measure the impact of POSEIDON criteria

BackgroundCurrently, there is no consensus on the optimal management of women with low prognosis in ART. In this Delphi consensus, a panel of international experts provided real-world clinical perspectives on a series of literature-supported consensus statements regarding the overall relevance of the POSEIDON criteria for women with low prognosis in ART.MethodsUsing a Delphi-consensus framework, twelve experts plus two Scientific Coordinators discussed and amended statements and supporting references proposed by the Scientific Coordinators (Round 1). Statements were distributed via an online survey to an extended panel of 53 experts, of whom 36 who voted anonymously on their level of agreement or disagreement with each statement using a six-point Likert-type scale (1 = Absolutely agree; 2 = More than agree; 3 = Agree; 4 = Disagree; 5 = More than disagree; 6 = Absolutely disagree) (Round 2). Consensus was reached if > 66% of participants agreed or disagreed.ResultsThe extended panel voted on seventeen statements and subcategorized them according to relevance. All but one statement reached consensus during the first round; the remaining statement reached consensus after rewording. Statements were categorized according to impact, low-prognosis validation, outcomes and patient management. The POSEIDON criteria are timely and clinically sound. The preferred success measure is cumulative live birth and key management strategies include the use of recombinant FSH preparations, supplementation with r-hLH, dose increases and oocyte/embryo accumulation through vitrification. Tools such as the ART Calculator and Follicle-to-Oocyte Index may be considered. Validation data from large, prospective studies in each POSEIDON group are now needed to corroborate existing retrospective data.ConclusionsThis Delphi consensus provides an overview of expert opinion on the clinical implications of the POSEIDON criteria for women with low prognosis to ovarian stimulation.

Reliability of the risk of bias assessment in randomized controlled trials for nursing: A cross-sectional study.

To evaluate the percentage and reasons for disagreements in the risk of bias (RoB) assessments for randomized controlled trials (RCTs) included in more than one Cochrane review in the field of nursing. Disagreement in RoB assessments reduces the credibility of the evidence summarized by systematic reviews (SRs). There is no study that evaluates the reliability of RoB assessments in nursing studies. Secondary data analysis based on research reports. RCTs included in more than one review in the nursing have been included. The disagreement of the assessment was analysed, and the possible reasons for disagreements were investigated. Twenty-three RCTs were included in more than one review. The agreement of assessment ranged from 36.84% for "selective reporting" to 91.30% for "random sequence generation". "Allocation concealment" showed the optimal agreement (84.21%). The items "blinding of participants and personnel", "blinding of outcome assessment" and "incomplete outcome data" showed poor agreement, with 50.00%, 58.82% and 66.67%, respectively. Most disagreements came from extracting incomplete or different RCTs' information. The level of agreement of the assessment between reviews has varied greatly in the field of nursing. More complete and accurate information of RCTs needs to be collected when conducting a SR.

Coastal marine habitats deterioration according to users’ perception: the case of Cap de Creus Marine Protected Area (NE Spain)

Assessing how different users of a Marine Protected Area perceive environmental changes can contribute to design management strategies. We assess how locals and tourists perceive environmental changes in the Cap de Creus protected area (NW Mediterranean, Spain). To identify locally perceived changes, we first conducted semi-structured interviews with locals (n = 38). Reported environmental changes were then used to construct a survey applied to locals and tourists (n = 427). In semi-structured interviews, environmental changes were the least reported changes compared to economic and social changes; reports of negative environmental changes dominated over reports of positive environmental changes. Overall, all survey participants reported a decline of the health status of the local environment, with locals displaying higher levels of agreement with statements referring to coastal environmental deterioration than tourists. The predominance of responses reporting economic versus environmental changes can be explained by the recent radical transformation of the area towards tourism. Reports of coastal marine area deterioration are in line with available instrumental data. Higher levels of agreement with statements referring to environmental deterioration among locals than among tourists highlight the importance of people’s connection with nature to assess change. Considering that Cap de Creus has been protected for more than two decades, our findings raise concerns regarding its ongoing deterioration and underscore the importance of monitoring the effectiveness of marine protection to modulate management strategies.

Definition and recommendations of advance care planning: A Delphi study in five Asian sectors.

In Confucian-influenced Asian societies, explicit end-of-life conversations are uncommon and family involvement in decision-making is crucial, which complicates the adoption of culturally sensitive advance care planning. To develop a consensus definition of advance care planning and provide recommendations for patient-centered and family-based initiatives in Asia. A five-round Delphi study was performed. The rating of a definition and 84 recommendations developed based on systematic reviews was performed by experts with clinical or research expertise using a 7-point Likert scale. A median = 1 and an inter-quartile range = 0-1 were considered very strong agreement and very strong consensus, respectively. The Delphi study was carried out by multidisciplinary experts on advance care planning in five Asian sectors (Hong Kong/Japan/Korea/Singapore/Taiwan). Seventy-seven of 115 (67%) experts rated the statements. Advance care planning is defined as "a process that enables individuals to identify their values, to define goals and preferences for future medical treatment and care, to discuss these values, goals, and preferences with family and/or other closely related persons, and health-care providers, and to record and review these preferences if appropriate." Recommendations in the domains of considerations for a person-centered and family-based approach, as well as elements, roles and tasks, timing for initiative, policy and regulation, and evaluations received high levels of agreement and consensus. Our definition and recommendations can guide practice, education, research, and policy-making in advance care planning for Asian populations. Our findings will aid future research in crafting culturally sensitive advance care planning interventions, ensuring Asians receive value-aligned care.

Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data

Hot subdwarf stars are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. In the Hertzsprung-Russell Diagram they are located by the blue end of the Horizontal Branch. Most models agree on a quite probable common envelope binary evolution scenario in the Red Giant phase. However, the current binarity rate for these objects is yet unsolved, but key, question in this field. This study aims to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The results will be compared with those obtained previously using Virtual Observatory techniques on coincident samples. The methods used for hot subdwarf binary classification include supervised and unsupervised machine learning techniques. Specifically, we have used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their colour-magnitude properties. Among these, 2815 objects have Gaia DR3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). In order to ensure spectral quality, previously to SOM and CNN classification, our 2815 BP/RP set were pre-analysed with two different approaches: the cosine similarity technique and the Uniform Manifold Approximation and Projection (UMAP) technique. Additional analysis onto a golden sample of 88 well-defined objects, is also presented. The findings demonstrate a high agreement level (sim 70-90<!PCT!>) with the classifications from the Virtual Observatory Sed Analyzer (VOSA) tool. This shows that the SVM, SOM, and CNN methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. Notably, SVM in a radial basis function achieves 70.97<!PCT!> reproducibility for binary targets using photometry, and CNN reaches 84.94<!PCT!> for binary detection using spectroscopy. We also found that the single--binary differences are especially observable on the infrared flux in our Gaia DR3 BP/BR spectra, at wavelengths larger than $ sim $700 nm. We find that all the methods used are in fairly good agreement and are particularly effective to discern between single and binary systems. The agreement is also consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods also appear effective for detecting peculiarities in the spectra. While promising, challenges in dealing with uncertain compositions highlight the need for caution, suggesting further research is needed to refine techniques and enhance automated classification reliability, particularly for large-scale surveys.

Improving agreement in assessing subtrochanteric fracture healing among orthopedic surgeons using the Radiographic Union Score for Hip (RUSH)

BackgroundPrompt identification of fracture healing is crucial for medical decision-making and clinical research. This study aims to assess the effectiveness of the Radiographic Union Score for Hip (RUSH) in subtrochanteric fractures and determine the optimal scoring threshold for fracture healing.MethodsWe obtained 94 sets of X-ray films from the postoperative follow-up of 35 patients who underwent intramedullary nail fixation for subtrochanteric fractures. Six orthopedic trauma surgeons evaluated the imaging data and determined fracture healing based on subjective judgment. They then scored the X-ray images using the RUSH form. After four weeks, the same observers re-evaluated 47 randomly selected sets of radiographs. Subsequently, the observers and study designer conducted a consensus meeting where they agreed on whether the fractures had healed. We used Fleiss kappa to evaluate agreement among observers based on subjective impressions, and the intraclass correlation coefficient assessed RUSH score consistency. Regression analysis examined the relationship between scores and fracture healing consensus using a receiver operator characteristic (ROC) curve.ResultsBased on overall impressions, the agreement level among orthopedic trauma surgeons for determining the healing status of subtrochanteric fractures was moderate (Kappa = 0.564, 95% CI: 0.511–0.616). However, utilizing the RUSH scoring system can improve the consistency to a substantial level of agreement (ICC = 0.748, 95% CI: 0.663–0.817). Regarding intraobserver reliability, there were significant differences among orthopedic trauma surgeons in judging fracture healing based on overall impressions. However, using the RUSH scoring form, the attending group achieved better intraobserver consistency than the resident group. Regression analysis and ROC curve analysis revealed a strong correlation between the RUSH total score, cortical bone score, cancellous bone score, and consensus on fracture healing. The AUC values are 0.769 (95% CI: 0.670–0.868), 0.779 (95% CI: 0.681–0.876), and 0.771 (95% CI: 0.674–0.867), respectively.ConclusionsOur study highlights the effectiveness of the RUSH scoring system in enhancing interobserver agreement and intraobserver consistency when evaluating subtrochanteric fracture healing in orthopedic trauma surgeons. We propose setting 21 points as the threshold for the simplified RUSH scoring system to determine fracture healing. This system includes only eight items related to cortical bone, with a total score of 24 points.

The Effectiveness of using Artificial Intelligence for Building Professional Competencies of Future Specialists in Art Majors in Higher Education Institutions

The research aims to study the impact of the use of artificial intelligence (AI) tools in higher education institutions (HEIs) on building professional competencies of future art specialists. The research employed quantitative and qualitative methods (in particular, modeling methods, pedagogical experiments, and survey of respondents to assess the impact of AI tools on building professional competencies). The author’s definition of the concept of “professional competencies of art specialists” is proposed. Targeted tools were selected and used for building components of professional competencies. For example, VocalAnalysis AI tools were used to form the perceptual component — for students majoring in Musical Art; Art Vision AI — for students majoring in Fine Arts; ChoreoVision AI — for students majoring in Choreography. The results of the study show that students rated their level of ability to use AI as higher than medium. The questionnaire designed to study the impact of the use of AI on building professional competencies of future specialists in art majors, demonstrated a high level of agreement between the assessment of the impact of the use of AI tools on the formation of various components of professional competencies. Further research can be aimed at the development and testing of an algorithm for objective expert evaluation of specific AI tools for the implementation of art projects by students of the specified art majors.

Differences in Accuracy Between Paramedics and Emergency Medical Technicians (EMTs) When Identifying Low-Acuity Pediatric Patients

Background Alternative emergency medical services (EMS) disposition programs have been developed for adults with low-acuity complaints. One barrier to including children in such programs is a lack of evidence regarding whether paramedics and emergency medical technicians (EMTs) can accurately identify children with low-acuity complaints. Our primary objective was to compare the accuracy of EMTs to that of paramedics in identifying low-acuity pediatric encounters. Our secondary objective was to determine whether support for alternative EMS dispositions differed between paramedics and EMTs. Methods This was a planned secondary analysis of a cross-sectional study of children transported by EMS to an emergency department (ED). Acuity was defined using a composite measure that included physiological patient assessments, resources used (including laboratory tests and radiographs), and patient disposition. EMS clinicians rated on a Likert scale their level of agreement that a patient had a low-acuity problem and could have been transported by private vehicle, seen in clinic, or not transported. The sensitivity, specificity, and positive and negative predictive values (PPV and NPV) for paramedic and EMT acuity assessments were calculated. Results EMS surveys were completed for 84.0% of 996 participants (76.4% by EMTs, 22.6% by paramedics). 35.1% of participants were classified as having a low-acuity complaint. The sensitivity for identifying a child as low-acuity was 24% (95% CI 20%, 38%) for paramedics and 50% (46%, 54%) for EMTs. The PPV for identifying a child as low-acuity was 0.44 (0.28, 0.60) for paramedics and 0.62 (0.55, 0.68) for EMTs. Only 20.5% of paramedics and 22.5% of EMTs supported non-transport for children with low-acuity complaints. Conclusions Relying on EMS assessments of pediatric patient acuity may lead to under-triage, regardless of whether a paramedic or EMT makes this determination. Additional training and triage tools may be required before children can be safely included in alternative EMS disposition programs.

Discrepancies in ChatGPT's Hip Fracture Recommendations in Older Adults for 2021 AAOS Evidence-Based Guidelines.

Background: This study aimed to assess the reproducibility and reliability of Chat-Based GPT (ChatGPT)'s responses to 19 statements regarding the management of hip fractures in older adults as adopted by the American Academy of Orthopaedic Surgeons' (AAOS) evidence-based clinical practice guidelines. Methods: Nineteen statements were obtained from the 2021 AAOS evidence-based clinical practice guidelines. After generating questions based on these 19 statements, we set a prompt for both the GPT-4o and GPT-4 models. We repeated this process three times at 24 h intervals for both models, producing outputs A, B, and C. ChatGPT's performance, the intra-ChatGPT reliability, and the accuracy rates were assessed to evaluate the reproducibility and reliability of the hip fracture-related guidelines. Regarding the strengths of the recommendation compared with the 2021 AAOS guidelines, we observed accuracy of 0.684, 0.579, and 0.632 for outputs A, B, and C, respectively. Results: The precision was 0.740, 0.737, and 0.718 in outputs A, B, and C, respectively. For the reliability of the strengths of the recommendation, the Fleiss kappa was 0.409, indicating a moderate level of agreement. No statistical differences in the strengths of the recommendation were observed in outputs A, B, and C between the GPT-4o and GPT-4 versions. Conclusion: ChatGPT may be useful in providing guidelines for hip fractures but performs poorly in terms of accuracy and precision. However, hallucinations remain an unresolved limitation associated with using ChatGPT to search for hip fracture guidelines. The effective utilization of ChatGPT as a patient education tool for the management of hip fractures should be addressed in the future.

Determining the New Norm Elements in Emergency Departments in Malaysia During a Pandemic: A Fuzzy Delphi Method.

Emergency departments (EDs) have had to cope with various pandemics, such as HIN1, Ebola and the currently ongoing COVID-19. This study aimed to determine the elements of the new norm that has introduced changes into healthcare systems, particularly EDs, during the COVID-19 pandemic and to obtain consensus from the experts, the Emergency physicians in EDs across Malaysia. No previous study has been conducted on this topic. This study used the fuzzy Delphi method (FDM) to acquire expert consensus. There were two phases in this study. The first involved extracting the elements to be assessed by the selected experts from a literature review. Three major domains were considered: infrastructure, governance and human resources. A total of 35 items were identified and divided into the three domains. In the second phase, the selected items were sent to a group of 15 ED physicians, who were asked to rate the items on a Likert-type scale. The data were then analysed using FDM. A total of 35 items were identified as possible new norms from a literature search for the three domains (governance, infrastructure and human resources). The first step of the FDM analysis showed that 9 out of the 35 items did not fulfil the initial requirement of the FDM, since the threshold value (d) must be lower than 0.2 (d </ 0.2). In meeting the second condition for the FDM, 25 out of the 35 items had an expert consensus of > 75%. Regarding the third requirement for FDM, only 1 out of the 35 items did not meet the criteria of an average fuzzy number (A value) of > 0.5. Finally, 25 items fulfilled all three requirements of FDM, so these were retained and the remaining 10 items were discarded. The FDM used in this study had identified 25 items achieved the required level of agreement by the chosen experts. The results of this study can be used to guide EDs in Malaysia to utilise the new norms items in mitigating major outbreak affecting the ED services.

Clinical validation of a deep learning-based approach for preoperative decision-making in implant size for total knee arthroplasty.

Orthopedic surgeons use manual measurements, acetate templating, and dedicated software to determine the appropriate implant size for total knee arthroplasty (TKA). This study aimed to use deep learning (DL) to assist in deciding the femoral and tibial implant sizes without manual manipulation and to evaluate the clinical validity of the DL decision by comparing it with conventional manual procedures. Two types of DL were used to detect the femoral and tibial regions using the You Only Look Once algorithm model and to determine the implant size from the detected regions using convolutional neural network. An experienced surgeon predicted the implant size for 234 patient cases using manual procedures, and the DL model also predicted the implant sizes for the same cases. The exact accuracies of the surgeon's template were 61.54% and 68.38% for predicting femoral and tibial implant sizes, respectively. Meanwhile, the proposed DL model reported exact accuracies of 89.32% and 90.60% for femoral and tibial implant sizes, respectively. The accuracy ± 1 levels of the surgeon and proposed DL model were 97.44% and 97.86%, respectively, for the femoral implant size and 98.72% for both the surgeon and proposed DL model for the tibial implant size. The observed differences and higher agreement levels achieved by the proposed DL model demonstrate its potential as a valuable tool in preoperative decision-making for TKA. By providing accurate predictions of implant size, the proposed DL model has the potential to optimize implant selection, leading to improved surgical outcomes.

An exploratory qualitative study on factors influencing the level of agreement in symptom reports in child-caregiver dyads.

Clinicians often rely on caregiver proxy symptom reports to treat cancer-related symptoms in children. Research has described disagreement between children's and caregivers' symptom reports. Factors influencing the level of agreement is an understudied area. Thus, this study aimed to examine potential factors contributing to the level of agreement between symptom reports provided by children and their caregivers. Sixteen child-caregiver dyads participated separately in semi-structured interviews after completing a brief symptom measure independently using an electronic device. Child and caregiver quantitative symptom responses were reviewed in real-time and incorporated into the semi-structured interview. Sample characteristics and the level of agreement between symptom reports were calculated using descriptive statistics. Transcribed participant interviews were analyzed using content analysis. Nearly half of child-caregiver dyads exhibited a moderate (37.5%, n = 6) or low (18.75%, n = 3) level of agreement on the abbreviated symptom measure. Qualitative analysis identified 5 themes: recognizing symptoms, experiencing symptoms, communicating symptoms, re-assessing and treating symptoms, and influencing individual and relationship factors. Influencing individual, including a child's tendencies or personality traits, and relationship factors intersected the other themes, partially explained their symptom perceptions, and served to facilitate or hinder symptom communication. Symptom communication is an important part of the symptom cycle, comprised of symptom recognition, experience, and management. Individual and relational factors may influence discrepancies in symptom perceptions between the child and caregiver. Clinicians and researchers should consider developing interventions to enhance symptom communication and promote collaboration between children and their caregivers to address symptom suffering during cancer treatment.

Perceptions and attitudes toward artificial intelligence among frontline physicians and physicians' assistants in Kansas: a cross-sectional survey.

This survey aims to understand frontline healthcare professionals' perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions. We conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants' familiarity and experience with AI on the survey results. From 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = -0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups. The findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals' intention to use AI, regardless of their AI familiarity. The study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals' perspective. These insights can guide strategies for successful AI implementation in healthcare.