Electronic Integrated Management of Childhood Illness (eIMCI): a randomized controlled trial to evaluate an electronic clinical decision-making support system for management of sick children in primary health care facilities in South Africa.

BACKGROUND: Electronic clinical decision-making support systems (eCDSS) aim to assist clinicians making complex patient management decisions and improve adherence to evidence-based guidelines. Integrated management of Childhood Illness (IMCI) provides guidelines for management of sick children attending primary health care clinics and is widely implemented globally. An electronic version of IMCI (eIMCI) was developed in South Africa. METHODS: We conducted a cluster randomized controlled trial comparing management of sick children with eIMCI to the management when using paper-based IMCI (pIMCI) in one district in KwaZulu-Natal. From 31 clinics in the district, 15 were randomly assigned to intervention (eIMCI) or control (pIMCI) groups. Computers were deployed in eIMCI clinics, and one IMCI trained nurse was randomly selected to participate from each clinic. eIMCI participants received a one-day computer training, and all participants received a similar three-day IMCI update and two mentoring visits. A quantitative survey was conducted among mothers and sick children attending participating clinics to assess the quality of care provided by IMCI practitioners. Sick child assessments by participants in eIMCI and pIMCI groups were compared to assessment by an IMCI expert. RESULTS: Self-reported computer skills were poor among all nurse participants. IMCI knowledge was similar in both groups. Among 291 enrolled children: 152 were in the eIMCI group; 139 in the pIMCI group. The mean number of enrolled children was 9.7 per clinic (range 7-12). IMCI implementation was sub-optimal in both eIMCI and pIMCI groups. eIMCI consultations took longer than pIMCI consultations (median duration 28 minutes vs 25 minutes; p = 0.02). eIMCI participants were less likely than pIMCI participants to correctly classify children for presenting symptoms, but were more likely to correctly classify for screening conditions, particularly malnutrition. eIMCI participants were less likely to provide all required medications (124/152; 81.6% vs 126/139; 91.6%, p= 0.026), and more likely to prescribe unnecessary medication (48/152; 31.6% vs 20/139; 14.4%, p = 0.004) compared to pIMCI participants. CONCLUSIONS: Implementation of eIMCI failed to improve management of sick children, with poor IMCI implementation in both groups. Further research is needed to understand barriers to comprehensive implementation of both pIMCI and eIMCI. (349) CLINICAL TRIALS REGISTRATION: Clinicaltrials.gov ID: BFC157/19, August 2019.

Evaluation of the Accuracy of the Smart Work Injury Management (SWIM) System to Assist Case Managers in Predicting the Work Disability of Injured Workers.

PURPOSE: Many countries have developed clinical decision-making support tools, such as the smart work injury management (SWIM) system in Hong Kong, to predict rehabilitation paths and address global issues related to work injury disability. This study aims to evaluate the accuracy of SWIM by comparing its predictions on real work injury cases to those made by human case managers, specifically with regard to the duration of sick leave and the percentage of permanent disability. METHODS: The study analyzed a total of 442 work injury cases covering the period from 2012 to 2020, dividing them into non-litigated and litigated cases. The Kruskal-Wallis post hoc test with Bonferroni adjustment was used to evaluate the differences between the actual data, the SWIM predictions, and the estimations made by three case managers. The intra-class correlation coefficient was used to assess the inter-rater reliability of the case managers. RESULTS: The study discovered that the predictions made by the SWIM model and a case manager possessing approximately 4 years of experience in case management exhibited moderate reliability in non-litigated cases. Nevertheless, there was no resemblance between SWIM's predictions regarding the percentage of permanent disability and those made by case managers. CONCLUSION: The findings indicate that SWIM is capable of replicating the sick leave estimations made by a case manager with an estimated 4 years of case management experience, albeit with limitations in generalizability owing to the small sample size of case managers involved in the study. IMPLICATIONS: These findings represent a significant advancement in enhancing the accuracy of CDMS for work injury cases in Hong Kong, signaling progress in the field.

[Efficacy and safety of artificial intelligence-based large language models for decision making support in herniology: evaluation by experts and general surgeons]. / Effektivnost' i bezopasnost' bol'shikh yazykovykh modelei na osnove iskusstvennogo intellekta v kachestve instrumenta podderzhki prinyatiya reshenii v gerniologii: otsenka ekspertami i obshchimi khirurgami.

OBJECTIVE: To evaluate the quality of recommendations provided by ChatGPT regarding inguinal hernia repair. MATERIAL AND METHODS: ChatGPT was asked 5 questions about surgical management of inguinal hernias. The chat-bot was assigned the role of expert in herniology and requested to search only specialized medical databases and provide information about references and evidence. Herniology experts and surgeons (non-experts) rated the quality of recommendations generated by ChatGPT using 4-point scale (from 0 to 3 points). Statistical correlations were explored between participants' ratings and their stance regarding artificial intelligence. RESULTS: Experts scored the quality of ChatGPT responses lower than non-experts (2 (1-2) vs. 2 (2-3), p<0.001). The chat-bot failed to provide valid references and actual evidence, as well as falsified half of references. Respondents were optimistic about the future of neural networks for clinical decision-making support. Most of them were against restricting their use in healthcare. CONCLUSION: We would not recommend non-specialized large language models as a single or primary source of information for clinical decision making or virtual searching assistant.

Actual situation of decision-making support from medical staff when cancer patients make treatment choices.

Background: We investigated factors involved in decision-making support provided by physicians, nurses, pharmacists and medical and psychiatric social workers involved in cancer care. Materials & methods: A questionnaire survey on decision-making support was conducted. The level of clinician support was classified as 'supporting patients' 'decision-making process regarding cancer treatment', 'no support for patients' 'decision-making process regarding cancer treatment' or 'team-based support for patients' 'decision-making process regarding cancer treatment'. Results: Physicians estimated that 83.7% of patients made a cancer treatment decision within 1 week, but 45.4% of patients had difficulty making a decision. Conclusion: Medical personnel should support patients who have difficulty making decisions, establish a screening method to identify those needing support and develop a system providing decision-making support through interprofessional work.

We conducted a survey to investigate issues related to the level of decision-making support provided by physicians, nurses, pharmacists medical social workers and psychiatric social workers involved in cancer care. The physicians reported that 83.7% of patients with cancer chose a treatment plan within 1 week, although 45.4% of patients had difficulty making a decision. These decision-making difficulties arose at the time of diagnosis, when having difficulty controlling adverse events and when cancer metastasis or recurrence occurred. Some medical providers supported patients who had particular difficulty in choosing their cancer treatment, others provided no support, while a third group orchestrated a team to support them in their decision-making. To improve the quality of decision-making support, interprofessional work should be promoted and screening tools to identify those who need support should be established.

Challenges of using e-health technologies to support clinical care in rural Africa: a longitudinal mixed methods study exploring primary health care nurses' experiences of using an electronic clinical decision support system (CDSS) in South Africa.

BACKGROUND: Electronic decision-making support systems (CDSSs) can support clinicians to make evidence-based, rational clinical decisions about patient management and have been effectively implemented in high-income settings. Integrated Management of Childhood Illness (IMCI) uses clinical algorithms to provide guidelines for management of sick children in primary health care clinics and is widely implemented in low income countries. A CDSS based on IMCI (eIMCI) was developed in South Africa. METHODS: We undertook a mixed methods study to prospectively explore experiences of implementation from the perspective of newly-trained eIMCI practitioners. eIMCI uptake was monitored throughout implementation. In-depth interviews (IDIs) were conducted with selected participants before and after training, after mentoring, and after 6 months implementation. Participants were then invited to participate in focus group discussions (FGDs) to provide further insights into barriers to eIMCI implementation. RESULTS: We conducted 36 IDIs with 9 participants between October 2020 and May 2021, and three FGDs with 11 participants in October 2021. Most participants spoke positively about eIMCI reporting that it was well received in the clinics, was simple to use, and improved the quality of clinical assessments. However, uptake of eIMCI across participating clinics was poor. Challenges reported included lack of computer skills which made simple tasks, like logging in or entering patient details, time consuming. Technical support was provided, but was time consuming to access so that eIMCI was sometimes unavailable. Other challenges included heavy workloads, and the perception that eIMCI took longer and disrupted participant's work. Poor alignment between recording requirements of eIMCI and other clinic programmes increased participant's administrative workload. All these factors were a disincentive to eIMCI uptake, frequently leading participants to revert to paper IMCI which was quicker and where they felt more confident. CONCLUSION: Despite the potential of CDSSs to increase adherence to guidelines and improve clinical management and prescribing practices in resource constrained settings where clinical support is scarce, they have not been widely implemented. Careful attention should be paid to the work environment, work flow and skills of health workers prior to implementation, and ongoing health system support is required if health workers are to adopt these approaches (350).

Measuring the worldwide spread of COVID-19 using a comprehensive modeling method.

BACKGROUND: With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models. METHODS: We collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model. RESULTS: All 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9-12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10-20% of the countries' populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner. CONCLUSIONS: We demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management.

Transformer-Based Approach to Melanoma Detection.

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.

[Decision-making process and evaluation of public health interventions]. / Démarche décisionnelle et évaluation des interventions de santé publique.

OBJECTIVE: We have designed a methodological framework for experts involved in the support of decision-making in public health interventions. METHODS: The methodological framework consists of four elements: 1) A series of nine questions, formulated in non-technical terms, relevant to assessment of the usefulness of an intervention, at a given time in a given context; 2) Translation of these questions into concepts related to the evaluation of interventions (definition of the intervention, its target and objective, potential and actual effectiveness, safety, efficiency, and equity); 3) Logical organization of the information needed to address and answer the questions; and 4) An algorithm to translate the available information into recommendations on the real usefulness of the intervention in the context in which the questions were raised. RESULTS: Each step is illustrated by questions raised about road safety interventions, screening, blood transfusion and measures proposed during the COVID-19 pandemic. CONCLUSION: Decision-making can be facilitated if experts provide decision-makers with a formal summary of the strengths and weaknesses of existing knowledge, based on an analysis of all facets of an intervention's potential usefulness.

Development and Evaluation of Options for Action to Progress on the SDG 6 Targets in Austria.

The Agenda 2030 of the United Nations stipulates an ambitious set of 17 Sustainable Development Goals (SDGs). They were globally agreed upon and demand coherent, context-specific implementation at the national level. To address the complexity of challenges therein, the Agenda is designed to be integrated, indivisible, and universal. The numerous multifaceted interactions in-between the SDGs and with corresponding measures pose a complex challenge for decision-makers implementing them worldwide that requires support for a comprehensive discourse in the science-society-policy arena. Research on the interactions between the SDGs has been flourishing and can help to understand where policy options might be most successfully located. A catalytic effect on several other goals is, e.g., often attributed to SDG 6 on water and sanitation. However, beyond the where to locate policy options, it is similarly important to understand how potential policy options would affect the SDGs and their targets. We developed eleven options and 85 measures as context-specific pathways to advance the SDG 6 Targets in Austria. As a country in the Global North and with a generally far-established water and sanitation infrastructure and management, this responds to the Agenda's demand for universal applicability and can serve as an example to illustrate potential challenges beyond basic infrastructure provision and management. The proposed options cover resources-oriented sanitation, blue-green-brown infrastructure, efficient use and integrated management of water resources, maintenance and restoration of ecological functions of inland waters, reduction of diffuse discharge of nutrients and problematic substances as well as trace substances, water, sanitation and hygiene in public spaces, groundwater protection, development cooperation as well as co-design and co-creation. Their effects on the SDG 6 Targets are evaluated using a 7-point-scale. The evaluation method is simple and practicable, and fosters discourse on the entire water cycle amongst the expert group applying the method. The evaluated effects on the targets are found to be unanimously positive or neutral, but trade-offs might arise when including other SDGs in the assessment, making an expansion of the evaluation necessary for coherent implementation. The results can be used as a baseline to support follow-up discussions with stakeholders and decision-makers.

Post-consumer plastic sorting infrastructure improvements planning: Scenario-based modeling of greenhouse gas savings with sustainable costs.

With the increasing share of waste material recovery, household plastic waste is one of the biggest problems. In most countries, mainly manual sorting is used. Meanwhile, new automated technologies are being developed to expand the range of classifiable types to increase material recovery. The overall automation of the sorting process can help the EU's established recycling targets to be effectively met. However, the new technologies are feasible only in the case of large-capacity centers, which must be conveniently located in the existing infrastructure. This paper presents a two-stage model aiming to modernize the current sorting infrastructure for plastic waste. The approach uses multi-criteria optimization to minimize environmental impact at a reasonable price. The result is the optimal location of new automatic sorting centers, and waste stream flows using existing manual sorting facilities. The model is applied through an initial case study inspired by the Czech Republic data. Optimization output proposes four new automatic sorting lines with a total capacity of 158 kt per year. In most cases, manual sorting is used to reduce the transported weight of plastic waste, while automatic sorting lines separate the remaining, hardly recognized part. More than 60% of separately collected plastic is sorted and determined for material recovery.

Machine learning-based triage to identify low-severity patients with a short discharge length of stay in emergency department.

BACKGROUND: Overcrowding in emergency departments (ED) is a critical problem worldwide, and streaming can alleviate crowding to improve patient flows. Among triage scales, patients labeled as "triage level 3" or "urgent" generally comprise the majority, but there is no uniform criterion for classifying low-severity patients in this diverse population. Our aim is to establish a machine learning model for prediction of low-severity patients with short discharge length of stay (DLOS) in ED. METHODS: This was a retrospective study in the ED of China Medical University Hospital (CMUH) and Asia University Hospital (AUH) in Taiwan. Adult patients (aged over 20 years) with Taiwan Triage Acuity Scale level 3 were enrolled between 2018 and 2019. We used available information during triage to establish a machine learning model that can predict low-severity patients with short DLOS. To achieve this goal, we trained five models-CatBoost, XGBoost, decision tree, random forest, and logistic regression-by using large ED visit data and examined their performance in internal and external validation. RESULTS: For internal validation in CMUH, 33,986 patients (75.9%) had a short DLOS (shorter than 4 h), and for external validation in AUH, there were 13,269 (82.7%) patients with short DLOS. The best prediction model was CatBoost in internal validation, and area under the receiver operating cha racteristic curve (AUC) was 0.755 (95% confidence interval (CI): 0.743-0.767). Under the same threshold, XGBoost yielded the best performance, with an AUC value of 0.761 (95% CI: 0.742- 0.765) in external validation. CONCLUSIONS: This is the first study to establish a machine learning model by applying triage information alone for prediction of short DLOS in ED with both internal and external validation. In future work, the models could be developed as an assisting tool in real-time triage to identify low-severity patients as fast track candidates.

Midwifery scale to support shared decision-making for unplanned pregnancies: A cross-sectional study.

Midwives significantly support women with unplanned pregnancies-promoting a shared perspective on the decision-making process. This study aimed to develop a scale to support midwives to self-assess their practice of this vital role. Following the derivation of scale items and pilot testing, the final version of the scale was administered to 531 midwives to establish internal consistency and construct criterion-related validity. Through exploratory factor analysis, 35 items with a five-factor structure were retained to form the midwifery practice self-assessment scale to promote shared decision-making in women with unplanned pregnancies. These factors illustrate midwives' general aptitude and competencies in understanding environmental factors, collaborating with significant others and the interprofessional group, forming rapport and problem sharing, focusing on consultation content, and promoting autonomous decision-making. There were high and low scores on the scales after attendance of the workshops to support the decision-making of women with unplanned pregnancies. The reliability analysis showed acceptable Cronbach's alpha values for the five factors, from 0.85 to 0.87. The scale was demonstrated to be a reliable and valid measure that would help improve the quality of midwives' practice.

[Decision-making support for elderly individuals with dementia in a geriatric health service facility: A questionnaire survey on concrete care methods to draw wills of individuals].

PURPOSE: The purpose of this study was to clarify the care methods used by healthcare staff in service facilities to draw up wills for elderly individuals with dementia in daily life and the final stage in decision-making support. METHODS: A questionnaire survey was conducted among healthcare staff in a geriatric health service facility in August 2020. RESULTS: There were 45 subjects (16 males [35.6%]; 29 females [64.4%]). The average age was 42.2 (±12.3) years old, and the mean number of years of experience in a geriatric health service facility was 17.4 (±10.7) years. Deathbed care was provided to ≥90% of the subjects. A factor analysis of items related to decision making in elderly individuals with dementia revealed the first factor to be "support and communication to draw wills", the second factor to be "support and communication for expression to realize decision making", and the third factor to be "understanding, communication, and family support for decision-making realization." The totals of each of these three factors and "having confidence in care focusing on the viewpoint of elderly individuals with dementia" were significantly different. The correction between the Personhood of Approaches to Dementia Questionnaire Japanese version and the three subscales of decision making in elderly individuals with dementia had a significant coefficient of correlation. CONCLUSION: More polite communication methods are necessary for the formation, expression, and realization of the intentions of elderly individuals with dementia to support their decision making.

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment.

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.

Water sustainability assessment from the perspective of sustainable development capitals: Conceptual model and index based on literature review.

Decision-making associated with the promotion of water sustainability is subjected to uncertainties arising, on the one hand, from the current capacity of understanding socio-environmental systems and their temporality, complexity and multidimensionality; and, on the other hand, from the need to represent key aspects of these systems through data and information. Such a representation is often supported by indicators, indices and systems of indicators for relevant aspects of the problem and for a scenario under analysis. In this context, the main aim of this study was to develop a novel system of indicators for water sustainability assessment in river basins. Literature review and content analysis were used to define an analytical structure for the problem from the perspective of the natural, social and built capitals of related sustainable development assessment. Each capital has been hierarchically subdivided into categories, subcategories and attributes, leading to 54 aspects for water sustainability assessment. A list of indicators was compiled from the scientific and technical literature, linked to attributes, and associated with valuation ranges. This led to the proposition of the River Basin Water Sustainability Index (RBWSI) and sub-indices associated with categories and subcategories of the model. The timeframe and territorial scale relevant for assessment were defined as five-to-ten year periods and areas under the jurisdiction of river basin management committees, respectively. The new model and index thus proposed may be useful in water sustainability diagnostic and prognostic studies, such as required to assess the effectiveness of river basin management actions aimed at promoting sustainable development.

[Protecting dignity: Reviewing the decision-supporting process for an isolated elderly individual living in a large housing complex].

AIM: To clarify the conditions under which dignity is maintained by reviewing the decision-supporting process for a case. METHODS: We conducted both a longitudinal epidemiological survey and action research in parallel in a large housing complex district in Tokyo, Japan, using the community-based participatory research framework. Through collaboration with community professionals, we supported an isolated elderly man who refused medical intervention for three years until his death. After his passing, we re-examined all of his records, conducted in-depth interviews with the community professionals, and held a conference to review the process of managing this individual. RESULTS: Concerning support for the decision-making, three conclusions were obtained from the data: 1) a decision is not always stated explicitly; 2) a decision should be supported by the team, because mind sometimes changes; and 3) supporting decision-making is a process in itself. For the maintenance of dignity in the medical setting, the following were kept in mind: medical context is not all that is important; supporters should wait for the right moment to intervene, and support should be provided to help the patient keep in touch with other people and the community. CONCLUSIONS: While precisely defining dignity can be difficult, we explored the conditions under which dignity could be maintained by reviewing the decision-supporting process for a single case. Geriatricians may encounter difficult and complex cases such as this in the clinical setting, but guidelines cannot cover such diverse cases.

Balancing competing interests and obligations in mental health-care practice and policy.

It is often challenging for mental health-care providers and health organizations to perform their various roles and to meet their varied obligations. In complex mental health-care circumstances the concurrent application of relevant ethical principles and values often leads to the emergence of completing obligations that need to be carefully weighed and balanced in the making of care-related decisions. Although some clinical circumstances, such as those potentially triggering the duty to warn, are adequately guided by existing rules based on legal precedents, there is a gap in decision-making support in other mental health-care domains. This article proposes that a set of targeted, decision-making approaches be developed to assist in the handling of specific, challenging circumstances. By way of illustration, two novel approaches are introduced; that is, choosing to work within a moral relational space of optimal therapeutic engagement (at the micro level of clinical practice), and the use of a health policy development approach that instantiates deliberative engagement (at the meso and macro levels of health organization).

Development of policies for Natura 2000 sites: a multi-criteria approach to support decision makers.

The aim of this study is to present a methodology to support decision makers in the choice of Natura 2000 sites needing an appropriate management plan to ensure a sustainable socio-economic development. In order to promote sustainable development in the Natura 2000 sites compatible with nature preservation, conservation measures or management plans are necessary. The main issue is to decide when only conservation measures can be applied and when the sites need an appropriate management plan. We present a case study for the Italian Region of Umbria. The methodology is based on a multi-criteria approach to identify the biodiversity index (BI), and on the development of a human activities index (HAI). By crossing the two indexes for each site on a Cartesian plane, four groups of sites were identified. Each group corresponds to a specific need for an appropriate management plan. Sites in the first group with a high level both of biodiversity and human activities have the most urgent need of an appropriate management plan to ensure sustainable development. The proposed methodology and analysis is replicable in other regions or countries by using the data available for each site in the Natura 2000 standard data form. A multi-criteria analysis is especially suitable for supporting decision makers when they deal with a multidimensional decision process. We found the multi-criteria approach particularly sound in this case, due to the concept of biodiversity itself, which is complex and multidimensional, and to the high number of alternatives (Natura 2000 sites) to be assessed.

Enhancing Diagnostic Support for Chiari Malformation and Syringomyelia: A Comparative Study of Contextualized ChatGPT Models.

OBJECTIVES: The rapidly increasing adoption of large language models in medicine has drawn attention to potential applications within the field of neurosurgery. This study evaluates the effects of various contextualization methods on ChatGPT's ability to provide expert-consensus aligned recommendations on the diagnosis and management of Chiari Malformation and Syringomyelia. METHODS: Native GPT4 and GPT4 models contextualized using various strategies were asked questions revised from the 2022 Chiari and Syringomyelia Consortium International Consensus Document. ChatGPT-provided responses were then compared to consensus statements using reviewer assessments of 1) responding to the prompt, 2) agreement of ChatGPT response with consensus statements, 3) recommendation to consult with a medical professional, and 4) presence of supplementary information. Flesch-Kincaid, SMOG, word count, and Gunning-Fog readability scores were calculated for each model using the quanteda package in R. RESULTS: Relative to GPT4, all contextualized GPTs demonstrated increased agreement with consensus statements. PDF+Prompting and Prompting models provided the most elevated agreement scores of 19 of 24 and 23 of 24, respectively, versus 9 of 24 for GPT4 (p=.021, p=.001). A trend toward improved readability was observed when comparing contextualized models at large to ChatGPT4, with significant decreases in average word count (180.7 vs 382.3, p<.001) and Flesch-Kincaid Reading Ease score (11.7 vs 17.2, p=.033). CONCLUSIONS: The enhanced performance observed in response to ChatGPT4 contextualization suggests broader applications of large language models in neurosurgery than what the current literature indicates. This study provides proof of concept for the use of contextualized GPT models in neurosurgical contexts and showcases the easy accessibility of improved model performance.

Evaluating the effectiveness of mitigation measures in environmental impact assessments: A comprehensive review of development projects in Korea.

Rapid urbanization and development projects in Korea have posed significant threats to biodiversity; thus, effective mitigation measures are required to preserve natural habitats. Nevertheless, the factors underlying variations in mitigation measure effectiveness according to the disturbance level and surrounding environmental conditions have not been clarified. This study evaluated the effectiveness of mitigation measures implemented in environmental impact assessments (EIAs) of development projects in Korea, with a focus on their effectiveness with respect to the disturbance level and surrounding environmental conditions. A review of 288 EIA reports from selected projects that implemented all 10 mitigation measures classified according to the Wildlife Conservation Comprehensive Plan was conducted. Using the biodiversity tipping point framework, the effects of mitigation measures on biodiversity were categorized into four levels and analyzed. Analysis of variance and redundancy analysis were then performed to discern the variance in mitigation measure effectiveness in terms of the disturbance level, surrounding environment, and species. The results revealed significant variations in the effectiveness of mitigation measures depending on the surrounding environment and disturbance level. Linear projects exhibited a clear impact on various species as the disturbance level increased, whereas area-based projects did not exhibit such pronounced effects. All species demonstrated a negative relationship with development duration, development area, and distance from urban centers. Notably, avian and amphibian species showed a strong negative correlation with the digital elevation model while reptiles and mammals exhibited a strong positive relationship with pre-development biodiversity and distance from protected areas, respectively. Mitigation measures play a key role in alleviating the adverse effects of development projects; therefore, our findings indicate the need for spatially tailored mitigation plans to augment their effectiveness.