The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.

This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

Development and Investigation of a Grasping Analysis System with Two-Axis Force Sensors at Each of the 16 Points on the Object Surface for a Hardware-Based FinRay-Type Soft Gripper.

The FinRay soft gripper achieves passive enveloping grasping through its functional flexible structure, adapting to the contact configuration of the object to be grasped. However, variations in beam position and thickness lead to different behaviors, making it important to research the relationship between structure and force. Conventional research using FEM simulations has tested various virtual FinRay models but replicating phenomena such as buckling and slipping has been challenging. While hardware-based methods that involve installing sensors on the gripper and the object to analyze their states have been attempted, no studies have focused on the tangential contact force related to slipping. Therefore, we developed a 16-way object contact force measurement device incorporating two-axis force sensors into each of the 16 segmented objects and compared the normal and tangential components of the enveloping grasping force of the FinRay soft gripper under two types of contact friction conditions. In the first experiment, the proposed device was compared with a device containing a six-axis force sensor in one segmented object, confirming that the proposed device has no issues with measurement performance. In the second experiment, comparisons of the proposed device were made under various conditions: two contact friction states, three object contact positions, and two object motion states. The results demonstrated that the proposed device could decompose and analyze the grasping force into its normal and tangential components for each segmented object. Moreover, low friction conditions result in a wide contact area with lower tangential frictional force and a uniform normal pushing force, achieving effective enveloping grasping.

Application of CO2-Soluble Polymer-Based Blowing Agent to Improve Supercritical CO2 Replacement in Low-Permeability Fractured Reservoirs.

Since reservoirs with permeability less than 10 mD are characterized by high injection difficulty, high-pressure drop loss, and low pore throat mobilization during the water drive process, CO2 is often used for development in actual production to reduce the injection difficulty and carbon emission simultaneously. However, microfractures are usually developed in low-permeability reservoirs, which further reduces the injection difficulty of the driving medium. At the same time, this makes the injected gas flow very fast, while the gas utilization rate is low, resulting in a low degree of recovery. This paper conducted a series of studies on the displacement effect of CO2-soluble foaming systems in low-permeability fractured reservoirs (the permeability of the core matrix is about 0.25 mD). For the two CO2-soluble blowing agents CG-1 and CG-2, the effects of the CO2 phase state, water content, and oil content on static foaming performance were first investigated; then, a more effective blowing agent was preferred for the replacement experiments according to the foaming results; and finally, the effects of the blowing agents on sealing and improving the recovery degree of a fully open fractured core were investigated at different injection rates and concentrations, and the injection parameters were optimized. The results show that CG-1 still has good foaming performance under low water volume and various oil contents and can be used in subsequent fractured core replacement experiments. After selecting the injection rate and concentration, the blowing agent can be used in subsequent fractured cores under injection conditions of 0.6 mL/min and 2.80%. In injection conditions, the foaming agent can achieve an 83.7% blocking rate and improve the extraction degree by 12.02%. The research content of this paper can provide data support for the application effect of a CO2-soluble blowing agent in a fractured core.

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation.

Introduction: Our research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. Methods: Our model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE). Results: The evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach. Discussion: Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) on a 5,000 set of CT scan images.

Development of best performance evaluation indicators based on value-based healthcare for general hospital nursing: A Delphi study.

OBJECTIVE: To establish and validate a set of best nursing practice indicators for medical groups in Chinese general hospitals based on value-based healthcare (VBHC) theory. DESIGN: A modified Delphi method. METHODS: This study engaged experienced clinical nurses from public hospitals and multidisciplinary experts in operations management. Through a literature review and structured brainstorming, a comprehensive framework for assessing best nursing practices in Chinese general hospital medical groups was developed. A modified Delphi method was then employed to establish an indicator framework, followed by the Combined Empowerment-MABAC method to weight and validate the indicators using empirical data collected between June 2023 and October 2023. The CREDES checklist guided the reporting of this study. RESULTS: Sixteen experts, each with at least 10 years of experience in nurse management from nine healthcare organizations, participated in two rounds of consultation. The experts' responses and suggestions for rewording, deleting and adding items were incorporated into each round. Of the 34 proposed indicators, 25 were approved, covering healthcare service capacity, efficiency, quality and safety, patient experience and cost. CONCLUSION: The Delphi survey reached a consensus on necessary actions to improve nursing performance. The developed indicator system provides a foundational framework for standardizing the monitoring of care quality and performance assessment in Chinese clinical healthcare groups, with broad applicability. RELEVANCE TO CLINICAL PRACTICE: This study provides a reliable basis for developing a nursing performance assessment database, offering crucial insights for measuring quality of care and improving patient value. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

Key performance indicators and benchmarks in MCI prehospital response using technological tools: a qualitative study assessing the perception of practitioners and tool developers.

PURPOSE: The aim of this study is to investigate the opinions and perspectives of The Novel Integrated Toolkit for Enhanced Prehospital Life Support and Triage in Challenging and Large Emergencies (NIGHTINGALE) end-users and tool developers regarding Key Performance Indicators (KPIs) and benchmarks that assess the prehospital response to Mass Casualty Incidents (MCIs) enhanced by the NIT-MR. METHODS: A qualitative study employing focus group discussions was conducted to collect opinions and perspectives of end-users and tool developers regarding KPIs and benchmarks in MCI response using the NIT-MR. The criteria considered for the selection and distribution of participants within the groups was the nature of their involvement within the NIGHTINGALE project and their familiarity with the tools to be discussed. RESULTS: Thirty-one participants from different countries were included. Four themes emerged during data analysis which are: definition/explanation is the personal understanding of participants of the term KPI, process of KPI development and relationship with User Requirements is the decision process for assigning KPIs to user requirements, benchmarking is the mental process of associating a benchmark to a KPI or for developing a benchmark, and technical/medical gap is the gap of understanding between each sides' fields. CONCLUSION: This study emphasized the need for a structured approach to using KPIs and bridging the gap between technological and medical worlds, taking the NIGHTINGALE project, funded by the European Union, which aims to develop a technological toolkit for first responders in mass casualty incidents as an example. These insights are crucial for enhancing disaster response.

Evaluating Literature Reviews Conducted by Humans Versus ChatGPT: Comparative Study.

BACKGROUND: With the rapid evolution of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT-4 (OpenAI), there is an increasing interest in their potential to assist in scholarly tasks, including conducting literature reviews. However, the efficacy of AI-generated reviews compared with traditional human-led approaches remains underexplored. OBJECTIVE: This study aims to compare the quality of literature reviews conducted by the ChatGPT-4 model with those conducted by human researchers, focusing on the relational dynamics between physicians and patients. METHODS: We included 2 literature reviews in the study on the same topic, namely, exploring factors affecting relational dynamics between physicians and patients in medicolegal contexts. One review used GPT-4, last updated in September 2021, and the other was conducted by human researchers. The human review involved a comprehensive literature search using medical subject headings and keywords in Ovid MEDLINE, followed by a thematic analysis of the literature to synthesize information from selected articles. The AI-generated review used a new prompt engineering approach, using iterative and sequential prompts to generate results. Comparative analysis was based on qualitative measures such as accuracy, response time, consistency, breadth and depth of knowledge, contextual understanding, and transparency. RESULTS: GPT-4 produced an extensive list of relational factors rapidly. The AI model demonstrated an impressive breadth of knowledge but exhibited limitations in in-depth and contextual understanding, occasionally producing irrelevant or incorrect information. In comparison, human researchers provided a more nuanced and contextually relevant review. The comparative analysis assessed the reviews based on criteria including accuracy, response time, consistency, breadth and depth of knowledge, contextual understanding, and transparency. While GPT-4 showed advantages in response time and breadth of knowledge, human-led reviews excelled in accuracy, depth of knowledge, and contextual understanding. CONCLUSIONS: The study suggests that GPT-4, with structured prompt engineering, can be a valuable tool for conducting preliminary literature reviews by providing a broad overview of topics quickly. However, its limitations necessitate careful expert evaluation and refinement, making it an assistant rather than a substitute for human expertise in comprehensive literature reviews. Moreover, this research highlights the potential and limitations of using AI tools like GPT-4 in academic research, particularly in the fields of health services and medical research. It underscores the necessity of combining AI's rapid information retrieval capabilities with human expertise for more accurate and contextually rich scholarly outputs.

Performance evaluation of Mexico's health system at the national and subnational level, 1990-2019: an analysis of the Health Access and Quality Index.

OBJECTIVES: This study aimed to comprehensively evaluate Mexico's health system performance from 1990 to 2019 utilising the Health Access and Quality Index (HAQI) as a primary indicator. STUDY DESIGN: A retrospective ecological analysis was performed using data from the Global Burden of Diseases, Injuries and Risk Factors Study (GBD) study and the National Population Council (CONAPO). METHODS: HAQI values for 1990, 2000, 2010, 2015, and 2019 were examined for each state in Mexico and three age groups (young, working, and post-working). Additionally, the marginalisation index was employed to assess inequalities in the HAQI distribution across states. The concentration index of the HAQI for each year was estimated, and the efficiency of states in producing the HAQI was evaluated using a data envelopment approach. RESULTS: Through the analysis of national and subnational data, results indicated an overall improvement in healthcare access and quality during the study period. Although differences in the HAQI value related to state marginalisation decreased from 1990 to 2015, by 2019, the inequality had returned to a level comparable to 2000. Efficiency in producing health (HAQI values) exhibited substantial heterogeneity and fluctuations in the ranking order over time. States such as Nuevo León consistently performed well, while others, such as Guerrero, Chihuahua, Mexico City, and Puebla, consistently underperformed. CONCLUSIONS: The findings from this study emphasise the necessity for nuanced strategies to address healthcare disparities and enhance the overall system performance. The study provides valuable insights for ongoing discussions about the future of Mexico's healthcare system, aiming to inform evidence-based policy decisions and improve the nationwide delivery of healthcare services.

Call for Specialty-Specific Benchmarks for Cross-Specialty Quality Measures in the Quality Payment Program.

The introduction of the Quality Payment Program (QPP) by the Centers for Medicare & Medicaid Services (CMS) played a critical role in the process of transitioning U.S. healthcare from a pay-for-service to a pay-for-performance system. Physicians can participate in the QPP through one of three reporting methods: the traditional merit-based incentive payment system (MIPS), MIPS Value Pathways (MVPs), or Advanced Alternative Payment Models (APMs). These reporting methods require physicians to submit data on quality measures, which are averaged to determine a total quality performance score, which is weighted along with other QPP measures related to self-performance to provide an aggregate final performance score. This final score is used to determine either a negative, neutral, or positive percentage modifier for the physician's Medicare reimbursement payments, which applies to the fiscal year two years following the year of reporting. Quality measures are either specialty-specific or cross-specialty, meaning that they are reportable by any physician specialty. No studies have compared performance across physician specialty categories on these measures. Critics argue that CMS has not ensured equitable reporting of cross-specialty quality measures due to the difference in emphasis on aspects of care of different physician specialties, potentially advantaging some. For example, family medicine physicians may score higher on the blood pressure control quality measure due to its relevance in their practice. Significant performance differences could highlight areas of improvement for certain physicians in certain specialties and guide balanced measure development. The QPP currently uses non-specialty-specific historical quality performance scores as benchmarks to determine current-year quality measure scores, likely leading to unfair comparisons. Establishing specialty-specific benchmarks for cross-specialty measures would promote equitable evaluation and fair competition among all participating physicians.

External Quality Assessment (EQA) scheme for serological diagnostic test for SARS-CoV-2 detection in Sicily Region (Italy), in the period 2020-2022.

OBJECTIVES: Since December 2019, worldwide public health has been exposed to a severe acute respiratory syndrome caused by Coronavirus-2. Serological testing is necessary for retrospective assessment of seroprevalence rates, and the determination of vaccine response and duration of immunity. For this reason, it was necessary to introduce a panel of tests able to identify and quantify Covid-19 antibodies. METHODS: As a Regional Reference Centre, the CRQ Laboratory (Regional Laboratory for the Quality Control) developed and conducted an External Quality Assessment (EQA) panel of assays, to evaluate the quality of various methods, that were used by 288 Sicilian laboratories, previously authorized on behalf of the Public Health Service. RESULTS: The performance test was based on pooled samples with different levels of concentration of antibodies. 97 , 98, and 95 % of the participating laboratories tested all samples correctly in 2020, 2021, and 2022 respectively. The best performance was observed in the test of total Ig. The general performance of laboratories improved over the years. CONCLUSIONS: The incorrect diagnosis had and could still have important implications on vaccination cycles. Only through the effort of laboratory professionals, and the extension of the EQA scheme, a better harmonization of methods, protocols, and thus results, to guarantee a better healthcare system, will be possible.

Personalized compression therapeutic textiles: digital design, development, and biomechanical evaluation.

Physical-based external compression medical modalities could provide sustainable interfacial pressure dosages for daily healthcare prophylaxis and clinic treatment of chronic venous disease (CVD). However, conventional ready-made compression therapeutic textiles (CTs) with improper morphologies and ill-fitting of pressure exertions frequently limit patient compliance in practical application. Therefore, the present study fabricated the personalized CTs for various subjects through the proposed comprehensive manufacturing system. The individual geometric dimensions and morphologic profiles of lower extremities were characterized according to three-dimensional (3D) body scanning and reverse engineering technologies. Through body anthropometric analysis and pressure optimization, the knitting yarn and machinery variables were determined as the digital design strategies for 3D seamless fabrication of CTs. Next, to visually simulate the generated pressure mappings of developed CTs, the subject-specific 3D finite element (FE) CT-leg modelings with high accuracy and acceptability (pressure prediction error ratio: 11.00% ± 7.78%) were established based on the constructed lower limb models and determined tissue stiffness. Moreover, through the actual in vivo trials, the prepared customized CTs efficiently (Sig. <0.05; ρ = 0.97) distributed the expected pressure requirements referring to the prescribed compression magnitudes (pressure error ratio: 10.08% ± 7.75%). Furthermore, the movement abilities and comfortable perceptions were evaluated subjectively for the ergonomic wearing comfort (EWC) assessments. Thus, this study promotes the precise pressure management and clinical efficacy for targeted users and leads an operable development approach for related medical biomaterials in compression therapy.

Performance evaluation of influenza a rapid antigen test and PCR among nasopharyngeal and oropharyngeal samples.

Objectives: Rapid antigen test (RAT) and polymerase chain reaction (PCR) using nasopharyngeal (NP) or oropharyngeal (OP) swab specimens are the two main testing techniques used for laboratory diagnosis of influenza in clinical practice. However, performance variations have been observed not only between techniques, but also between different specimens. This study evaluated the differences in performance between specimens and testing techniques to identify the best combination in clinical practice. Methods: Both NP and OP samples from suspected influenza patients collected in the 2023/4-2023/5 Flu-season in Xiamen, China, were tested for RAT and quantitative PCR. The testing performance of the different specimens and testing techniques were recorded and evaluated. Results: Compared to PCR, RAT showed 58.9 % and 10.3 % sensitivity for NP and OP swabs, respectively. The Limit of Detection (LoD) was 28.71 the Median Tissue Culture Infectious Dose (TCID50)/mL. Compared with PCR using NP swabs, PCR with OP swabs showed 89.5 % sensitivity and 95.4 % specificity. Conclusions: There were no significant differences in performance between the specimens when PCR was used to test for influenza. However, a decrease in sensitivity was observed when the RAT was used, regardless of the specimen type. Therefore, to avoid false-negative results, PCR may be a better choice when OP swabs are used as specimens. In contrast, NP swabs should be the recommended specimens for RAT.

Performance evaluation of the MAGLUMI Hepatitis B virus surface antigen chemiluminescence immunoassay.

A highly sensitive and reliable Hepatitis B virus surface antigen (HBsAg) measurement is essential to universal screening, timely diagnosis, and management of Hepatitis B virus (HBV) infection. This study aimed to evaluate the performance of MAGLUMI HBsAg chemiluminescence immunoassay (CLIA). MAGLUMI HBsAg (CLIA) was compared against ARCHITECT HBsAg. 411 HBsAg positive samples, including different stages of infection, genotypes, subtypes, mutants, and 30 seroconversion panels were tested to evaluate diagnostic sensitivity. Diagnostic specificity was evaluated by testing 205 hospitalized samples and 5101 blood donor samples. Precision, limit of blank (LoB), limit of detection (LoD), and linearity were also verified. The diagnostic sensitivity of the MAGLUMI HBsAg (CLIA) was 100% with better seroconversion sensitivity than ARCHITECT HBsAg. The MAGLUMI HBsAg (CLIA) has optimal detection efficacy for HBV subgenotypes samples. The analytical sensitivity is 0.039 IU/mL. The initial diagnostic specificity is 99.63% on blood donors and 96.59% on hospitalized samples. The verification data demonstrated high repeatability, a LoB of 0.02 IU/mL, LoD of 0.05 IU/mL and an excellent linearity of 0.050-250 IU/mL (R2 = 0.9946). The MAGLUMI HBsAg (CLIA) is proved a highly sensitive and reliable assay with optimal subgenotype detection efficacy.

Developing environmental, social and governance (ESG) strategies on evaluation of municipal waste disposal centers: A case of Mexico.

Waste disposal systems are crucial components of environmental management, and focusing on this sector can contribute to the development of various other sectors and improve social welfare. Urban waste is no longer solely an environmental issue; it now plays a significant role in the economy, energy, and value creation, with waste disposal centers (WDCs) being a key manifestation. The purpose of this study is to measure the performance of WDCs in the state of Nuevo León, Mexico, with the aim of developing environmental, social, and governance (ESG) strategies to strengthen and prepare the WDCs for the industrial developments in this state. By identifying environmental variables and undesirable factors, the efficiency and managerial capacity of 32 WDCs were assessed. The analysis revealed that 9 out of the 32 WDCs are technically efficient, while the remaining 23 require significant improvements. Using the Data Envelopment Analysis (DEA) technique, an average efficiency score of 0.91 was found, with a standard deviation of 0.08. The managerial capacity analysis indicated that the highest-ranked WDC achieved an efficiency score of 1, whereas the lowest-ranked WDC scored 0.67. Finally, an operational map of development strategies was developed using the Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) approach. The results indicate that four phases of development should be followed for real development and maturity of development in these WDCs, including Groundwork, Structuring, Development and Growth, and Smart Maturity.

Key performance indicators in pre-hospital response to disasters and mass casualty incidents: a scoping review.

PURPOSE: The objective of this study was to offer a comprehensive synthesis of the existing Key performance indicators (KPIs) used in the evaluation of the pre-Hospital response to disasters and mass casualty incidents (MCIs). METHODS: At the end of December 2022 a scoping review has been performed on PubMed, Scopus, Embase, and Medline to identify articles describing the use of KPIs to assess the performance of first responders during the prehospital phase of an MCI (real or simulated). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, fourteen articles were included in the analysis. RESULTS: Eleven articles applied indicators in exercises and/or simulations. Two articles proposed new KPIs, and one used KPIs for developing a model for benchmarking pre-Hospital response. All articles analyzed quantitative indicators of time, whereas two studied indicators of structure, of process, and of outcome as well. CONCLUSION: The findings from this review emphasize the need for employing common terminology and using uniformed data collection tools, if obtaining standardized evaluation method is the goal to be achieved.

Multi-Tracking Sensor Architectures for Reconstructing Autonomous Vehicle Crashes: An Exploratory Study.

With the continuous development of new sensor features and tracking algorithms for object tracking, researchers have opportunities to experiment using different combinations. However, there is no standard or agreed method for selecting an appropriate architecture for autonomous vehicle (AV) crash reconstruction using multi-sensor-based sensor fusion. This study proposes a novel simulation method for tracking performance evaluation (SMTPE) to solve this problem. The SMTPE helps select the best tracking architecture for AV crash reconstruction. This study reveals that a radar-camera-based centralized tracking architecture of multi-sensor fusion performed the best among three different architectures tested with varying sensor setups, sampling rates, and vehicle crash scenarios. We provide a brief guideline for the best practices in selecting appropriate sensor fusion and tracking architecture arrangements, which can be helpful for future vehicle crash reconstruction and other AV improvement research.

Low-carbon development path based on carbon emission accounting and carbon emission performance evaluation: a case study of Chinese coal production enterprises.

Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11-2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise's low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise's economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect.

Optimizing Hospital Performance Evaluation in Total Weight Loss Outcomes After Bariatric Surgery: A Retrospective Analysis to Guide Further Improvement in Dutch Hospitals.

INTRODUCTION: Bariatric surgery aims for optimal patient outcomes, often evaluated through the percentage total weight loss (%TWL). Quality registries employ funnel plots for outcome comparisons between hospitals. However, funnel plots are traditionally used for dichotomous outcomes, requiring %TWL to be dichotomized, potentially limiting feedback quality. This study evaluates whether a funnel plot around the median %TWL has better discriminatory performance than binary funnel plots for achieving at least 20% and 25% TWL. METHODS: All hospitals performing bariatric surgery were included from the Dutch Audit for Treatment of Obesity. A funnel plot around the median was constructed using 5-year %TWL data. Hospitals positioned above the 95% control limit were colored green and those below red. The same hospitals were plotted in the binary funnel plots for 20% and 25% TWL and colored according to their performance in the funnel plot around the median. We explored the hospital's procedural mix in relation to %TWL performance as possible explanatory factors. RESULTS: The median-based funnel plot identified four underperforming and four outperforming hospitals, while only one underperforming and no outperforming hospitals were found with the binary funnel plot for 20% TWL. The 25% TWL binary funnel plot identified two underperforming and three outperforming hospitals. The proportion of sleeve gastrectomies performed per hospital may explain part of these results as it was negatively associated with median %TWL (ß = - 0.09, 95% confidence interval [- 0.13 to - 0.04]). CONCLUSION: The funnel plot around the median discriminated better between hospitals with significantly worse and better performance than funnel plots for dichotomized %TWL outcomes.

The development of a performance evaluation index system for Chinese Centers for Disease Control and Prevention: a Delphi consensus study.

BACKGROUND: The performance evaluation of the Centers for Disease Control and Prevention (CDC) is crucial for enhancing the quality of public health services. With the ongoing reform of the CDC system in China, the existing performance evaluation system faces challenges. This study used the Delphi method to develop a new performance evaluation system for China's provincial, city, and county-level CDC. METHODS: Following the "Structure-Process-Outcome" model, assessment indicators were systematically collected. Indicators were modified and screened through two Delphi rounds based on CDC responsibilities, health development, and national policies. Twenty-four experts provided ratings and recommendations, and the research team evaluated questionnaire reliability, expert positivity, expert authority, and opinion consistency. RESULTS: The preliminary index system identified through the literature review and pre-survey included 11 primary, 30 secondary, and 64 tertiary indicators. After the first round of consultation, two secondary indicators and 11 tertiary indicators were removed and 22 tertiary indicators were added. After the second round of consultation, three secondary indicators and 11 tertiary indicators were removed and three tertiary indicators were added, at which point the p-value of the test for Kendall's coefficient of concordance W was < 0.001 and the coefficient of variation was within acceptable limits (< 0.25), so the consultation was concluded. The final index system included 11 primary, 25 secondary, and 67 tertiary indicators. CONCLUSIONS: This study responded to the CDC system reform by developing a comprehensive performance evaluation index system for provincial, city, and county-level CDC in China. The index system is both scientifically grounded and practical, serving as an effective tool for promoting the high-quality work of CDC organizations.

AI and machine learning in medical imaging: key points from development to translation.

Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.