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BACKGROUND: Mandibular advancement devices (MADs) are considered a primary alternative treatment for adults with moderate to severe obstructive sleep apnea (OSA) who are unable to tolerate or do not respond to continuous positive airway pressure (CPAP) therapy, supported by substantial scientific evidence. While a range of designs and materials for MADs are commercially available, there is a lack of clear diagnostic guidelines to assist clinicians in selecting the most appropriate device based on a multidisciplinary evaluation of OSA patients. This narrative review seeks to outline the key characteristics of MADs that clinicians should evaluate during both the diagnostic and treatment phases for patients with OSA. METHODS: An extensive search of academic databases was conducted to gather relevant studies that address therapeutic and diagnostic recommendations for the design and titration of MADs. The search was carried out across EMBASE, Scopus, PubMed, and Web of Science up to May 2024. From a total of 1445 identified citations, 1103 remained after duplicate removal. Based on the inclusion criteria, the full text of 202 articles was retrieved, and 70 studies were ultimately included in this review. The extracted data were organized to generate clinical insights, aimed at guiding orthodontists in optimizing diagnostic and decision-making processes for treating OSA patients with MADs. RESULTS: The analysis led to the identification of key clinical questions that can assist orthodontists in enhancing their approach and choosing the appropriate appliance basing on the diagnosis and clinical dento-orofacial characteristics. CONCLUSIONS: Bibloc appliances could be preferred over mono-bloc devices due to the possibility of arranging the mandibular advancement according to the patient's clinical condition and orofacial symptoms. Provisional devices could be used as screening tools to verify the patient's adherence to the therapy. Regardless of the MAD design, type and programmed advancement, it must be under-lined that the rule of the orthodontist/dental specialist is secondary to the other sleep-medicine specialists (ORL, pulmonologist) and must be related to (1) a preliminary assessment of MAD usage (dental anatomical conditions), (2) testing a diagnostic MAD usable during a sleep examination (PSG or DISE), (3) final treatment with a definitive MAD.

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BACKGROUND: Artificial intelligence (AI) can be a tool in the diagnosis and acquisition of knowledge, particularly in dentistry, sparking debates on its application in clinical decision-making. OBJECTIVE: This study aims to evaluate the accuracy, completeness, and reliability of the responses generated by Chatbot Generative Pre-Trained Transformer (ChatGPT) 3.5 in dentistry using expert-formulated questions. MATERIALS AND METHODS: Experts were invited to create three questions, answers, and respective references according to specialized fields of activity. The Likert scale was used to evaluate agreement levels between experts and ChatGPT responses. Statistical analysis compared descriptive and binary question groups in terms of accuracy and completeness. Questions with low accuracy underwent re-evaluation, and subsequent responses were compared for improvement. The Wilcoxon test was utilized (α = 0.05). RESULTS: Ten experts across six dental specialties generated 30 binary and descriptive dental questions and references. The accuracy score had a median of 5.50 and a mean of 4.17. For completeness, the median was 2.00 and the mean was 2.07. No difference was observed between descriptive and binary responses for accuracy and completeness. However, re-evaluated responses showed a significant improvement with a significant difference in accuracy (median 5.50 vs. 6.00; mean 4.17 vs. 4.80; p=0.042) and completeness (median 2.0 vs. 2.0; mean 2.07 vs. 2.30; p=0.011). References were more incorrect than correct, with no differences between descriptive and binary questions. CONCLUSIONS: ChatGPT initially demonstrated good accuracy and completeness, which was further improved with machine learning (ML) over time. However, some inaccurate answers and references persisted. Human critical discernment continues to be essential to facing complex clinical cases and advancing theoretical knowledge and evidence-based practice.

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OBJECTIVES: This systematic literature review aimed to explore experiences worldwide of societal preferences integration into health technology assessments (HTAs) for rare diseases (RDs) and orphan drugs (ODs) through the implementation of multicriteria decision analysis (MCDA), discrete choice experiments (DCEs), and person trade-off (PTO) methods, among others. METHODS: A systematic search of the literature was conducted in April 2021 using PubMed, Cochrane, Embase, and Scopus databases. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach was used for the review phases. Finally, the Promoting Action on Research Implementation in Health Services framework was used to discuss the implementation of these instruments in the RD context. RESULTS: A total of 33 articles met the inclusion criteria. The studies measured societal preferences for RD and OD as part of HTA using MCDA (n = 17), DCE (n = 8), and PTO (n = 4), among other methods (n = 4). These found that patients and clinicians do not prioritize funding based on rarity. The public is willing to allocate funds only if the OD demonstrates effectiveness and improves the quality of life, considering as relevant factors disease severity, unmet health needs, and quality of life. Conversely, HTA agency experts preferred their current approach, placing more weight on cost-effectiveness and evidence quality, even though they expressed concern about the fairness of the drug review process. CONCLUSIONS: MCDA, PTO, and DCE are helpful and transparent methods for assessing societal preferences in HTA for RD and OD. However, their methodological limitations, such as arbitrary criteria selection, subjective scoring methods, framing effects, weighting adaptation, and value measurement models, could make implementation challenging.

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A complex Polytopic fuzzy set (CPoFS) extends a Polytopic fuzzy set (PoFS) by handling vagueness with degrees that range from real numbers to complex numbers within the unit disc. This extension allows for a more nuanced representation of uncertainty. In this research, we develop Complex Polytopic Fuzzy Sets (CPoFS) and establish basic operational laws of CPoFS. Leveraging these laws, we introduce new operators under a confidence level, including the confidence complex Polytopic fuzzy Einstein weighted geometric aggregation (CCPoFEWGA) operator, the confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (CCPoFEOWGA) operator, the confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (CCPoFEHGA) operator, the induced confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (I-CCPoFEOWGA) operator and the induced confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (I-CCPoFEHGA) operator, enhancing decision-making precision in uncertain environments. We also investigate key properties of these operators, including monotonicity, boundedness, and idempotency. With these operators, we create an algorithm designed to solve multiattribute decision-making problems in a Polytopic fuzzy environment. To demonstrate the effectiveness of our proposed method, we apply it to a numerical example and compare its flexibility with existing methods. This comparison will underscore the advantages and enhancements of our approach, showing its efficiency in managing complex decision-making scenarios. Through this, we aim to demonstrate how our method provides superior performance and adaptability across different situations.

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The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI's findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the "black box phenomenon") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.

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BACKGROUND: Family caregivers have a vital role to play in palliative care for chronically ill patients. In Taiwan, caregiver demographics are evolving, with the number of male caregivers increasing. Gender differences influence psychosocial behaviours, thought processes and communication styles. In healthcare, acknowledgement of gender differences facilitates effective delivery of high-quality care. AIM: The aim of this study is to explore male caregivers' decision-making process for palliative care for chronically ill family members. METHODS: This study employed grounded theory to generate a substantive theory of male caregivers' decision-making process for palliative care for chronically ill family members. We recruited 22 male participants from three inner-city teaching hospitals in Taiwan. FINDINGS: Regarding the decision-making process of palliative care of chronic ill family, where male caregivers do not want their loved ones suffering anymore, the male caregivers' decision-making process was impacted, first, by caregivers' views on the last stage of life; second, by their wish for good care during the end of life; and third, by their conviction that the patients' wishes should be respected. Furthermore, caregivers' philosophy of life and death is also a supportive ground for decision-making. This philosophy was influenced by their education in palliative care, financial status and religious beliefs and practices. The core category emerging from this study is encapsulated by a participant's assertion, 'How difficult is it? There are no male and female differences'. CONCLUSION: We found that palliative care experiences of male caregivers are important for the decision-making process for palliative care for their chronically ill family members. Caregivers want their loved ones to receive good care as the last step in life, to respect their wishes and no more suffering for the patient. Therefore, health professionals should be familiar with the palliative care process that caregivers go through to offer updated information when needed.

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Artificial intelligence (AI) is a multidisciplinary field intersecting computer science, cognitive science, and other disciplines, able to address the creation of systems that perform tasks generally requiring human intelligence. It consists of algorithms and computational methods that allow machines to learn from data, make decisions, and perform complex tasks, aiming to develop an intelligent system that can work independently or collaboratively with humans. Since AI technologies may help physicians in life-threatening disease prevention and diagnosis and make treatment smart and more targeted, they are spreading in health services. Indeed, humans and machines have unique strengths and weaknesses and can complement each other in providing and optimizing healthcare. However, the healthcare implementation of these technologies is related to emerging ethical and deontological issues regarding the fearsome reduction of doctors' decision-making autonomy and acting discretion, generally strongly conditioned by cognitive elements concerning the specific clinical case. Moreover, this new operational dimension also modifies the usual allocation system of responsibilities in case of adverse events due to healthcare malpractice, thus probably imposing a redefinition of the established medico-legal assessment criteria of medical professional liability. This article outlines the new challenges arising from AI healthcare integration and the possible ways to overcome them, with a focus on Italian legal framework. In this evolving and transitional context emerges the need to balance the human dimension with the artificial one, without mutual exclusion, for a new concept of medicine "with" machines and not "of" machines.

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Decision-making capability is essential in fulfilling the need for autonomy of people with intellectual disability. In this study we aimed to examine decision-making capability regarding important social situations in people with intellectual disability at different stages of decision-making process. We studied 80 vocational school students with mild intellectual disability and 80 students of a similar age from mass vocation schools. We assessed decision-making with Important Life Decisions Task (ILDT). Students with intellectual disability obtained significantly lower scores than controls for each of the stories in ILDT as in each stage and overall final score in the decision-making process. The magnitude of difference in scores between groups varied in different stages of decision-making process. The most notable difficulties in decision-making regarding important social situations in people with intellectual disability are related to the evaluation of alternatives stage. Pattern of differences obtained in our study may be related to the content of decision-making problems.

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Background: Genetic testing for cardiovascular diseases (CVD) is vital, but is underutilized in Japan due to limited insurance coverage, accessibility, and public disinterest. This study explores demographic factors influencing the decision to undergo CVD genetic testing. Methods and Results: We compared the CVD history and baseline demographics of Japanese adults who underwent genetic testing with those who did not, using an Internet survey. The regression model indicated that men, the young, married individuals, parents, and those with CVD, higher score for rationality, and lower quality of life were more inclined to undergo testing. Conclusions: Targeting strategies for CVD genetic testing could focus on these demographics.

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BACKGROUND: Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML). OBJECTIVE: This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system's outputs to analyze usability aspects and obtain insights related to future implementation. METHODS: A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients' scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients' data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems. RESULTS: The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024. CONCLUSIONS: This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52744.

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The Covid-19 pandemic revealed the difficulties of vaccinating a population under the circumstances marked by urgency and limited availability of doses while balancing benefits associated with distinct guidelines satisfying specific ethical criteria. We offer a vaccination strategy that may be useful in this regard. It relies on the mathematical concept of envy-freeness. We consider finding balance by allocating the resource among individuals that seem heterogeneous concerning the direct and indirect benefits of vaccination, depending on age. The proposed strategy adapts a constructive approach in the literature based on Sperner's Lemma to point out an approximate division of doses guaranteeing that both benefits are optimized each time a batch becomes available. Applications using data about population age distributions from diverse countries suggest that, among other features, this strategy maintains the desired balance, throughout the entire vaccination period. We discuss complementary aspects of the method in the context of epidemiological models of age-stratified Susceptible - Infected - Recovered (SIR) type.

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OBJECTIVE: To identify factors contributing to baseline knowledge in women with BRCA1/2 pathogenic variants (PVs) and knowledge gain after decision aid (DA) use. METHODS: Women with PVs in BRCA1 or BRCA2 genes were randomly assigned to an intervention group (IG) receiving DAs or a control group (CG). Of the total sample, 417 completed the baseline survey and were included in this analysis. Two multiple regression analyses were conducted: baseline data on socio-demographic, medical, decision-related and psychological variables were used to identify predictors for (1) baseline knowledge within the total group and (2) knowledge gain within the IG after DA use three months post study inclusion. RESULTS: At baseline, higher education status, no breast cancer history, and lower decisional conflict related to higher knowledge within the total group. After DA use within the IG, higher baseline scores for decisional conflict predicted higher knowledge gain, and higher baseline scores for depression and intrusion predicted lower knowledge gain. CONCLUSIONS: This study identified predictors of baseline knowledge and knowledge gain after DA use in women with BRCA1/2 PVs. PRACTICE IMPLICATIONS: Awareness of facilitating and hindering factors on these women's knowledge can improve understanding of their health literacy and enable further targeted support interventions.

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Clinical reasoning, specifically diagnostic decision-making, has been a subject of fragmented literature since the 1970s, marked by diverse theories and conflicting perspectives. This article reviews the latest evidence in medical education, drawing from scientific literature, to offer ophthalmologists insights into optimal strategies for personal learning and the education of others. It explores the historical development of clinical reasoning theories, emphasising the challenges in understanding how doctors formulate diagnoses. The importance of clinical reasoning is underscored by its role in making accurate diagnoses and preventing diagnostic errors. The article delves into the dual process theory, distinguishing between type 1 and type 2 thinking and their implications for clinical decision-making. Cognitive load theory is introduced as a crucial aspect, highlighting the limited capacity of working memory and its impact on the diagnostic process. The zone of proximal development (ZPD) is explored as a framework for optimal learning environments, emphasising the importance of scaffolding and deliberate practice in skill development. The article discusses semantic competence, mental representation, and the interplay of different memory stores-semantic, episodic, and procedural-in enhancing diagnostic proficiency. Self-regulated learning (SRL) is introduced as a student-centric approach, emphasising goal setting, metacognition, and continuous improvement. Practical advice is provided for minimising cognitive errors in clinical reasoning, applying dual process theory, and considering cognitive load theory in teaching. The relevance of deliberate practice in ophthalmology, especially in a rapidly evolving field, is emphasised for continuous learning and staying updated with advancements. The article concludes by highlighting the importance of clinical supervisors in recognising and supporting trainees' self-regulated learning and understanding the principles of various teaching and learning theories. Ultimately, a profound comprehension of the science behind clinical reasoning is deemed fundamental for ophthalmologists to deliver high-quality, evidence-based care and foster critical thinking skills in the dynamic landscape of ophthalmology.

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Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.

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AIM: The role of parents in decision-making concerning their child's end-of-life care is not clearly defined. Their participation is encouraged by ethical reflection, in particular by the CCNE (French National Ethics Advisory Committee), but laws are limited to imposing a duty to provide information to doctors. Decisions are taken at the end of a collegial meeting (CM) intended to better inform the child's referring physician (RP) who is in charge of the final decision following the French law. The aim of this study was to describe the support provided to bereaved families after they had been invited to attend a CM concerning their child, if they so wished. Additional aims were to determine the differences resulting from their acceptance or their refusal to participate as regards their perception of their child's history and as regards their grieving process. MATERIAL AND METHOD: We conducted a retrospective study of all CMs held between November 2016 and May 2021, drawing a distinction between proposals made or not made to parents and their decision to accept or refuse. RESULTS: In total, 49 CMs concerning 46 children were held during the study period. The proposal was not made to the parents in three cases; the parents chose to be present in 28 cases. The psychological follow-up (15/28 parents attending, 10/16 parents absent) illustrated that their presence enabled them to reflect on their child's death after having listened to and understood the reasons why it happened. They did not dispute the team's approach or decisions taken. CONCLUSION: It is possible to include parents in CMs if they so wish. It would appear more beneficial than merely providing them with the information required. Studies must be carried out to ensure potential long-term benefit.

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The demographic shift, together with financial constraint, justify a re-evaluation of the trajectory of care of very old critically ill patients (VIP), defined as older than 80 years. We must avoid over- as well as under-utilisation of critical care interventions in this patient group and ensure the inclusion of health care professionals, the patient and their caregivers in the decision process. This new integrative approach mobilises expertise at each step of the process beginning prior to intensive care unit (ICU) admission and extending to long-term follow-up. In this review, several international experts have contributed to provide recommendations that can be universally applied. Our aim is to define a minimum core dataset of information to be shared and discussed prior to ICU admission and to facilitate the shared-decision-making process with the patient and their caregivers, throughout the patient journey. Documentation of uncertainty may contribute to a tailored level of care and ultimately to discussions around possible limitations of life sustaining treatments. The goal of ICU care is not only to avoid death, but more importantly to maintain an acceptable quality of life and functional autonomy after hospital discharge. Societal consideration is important to highlight, together with alternatives to ICU admission. We discuss challenges for the future and potential areas of research. In summary, this review provides a state-of-the-art current overview and aims to outline future directions to address the challenges in the treatment of VIP.

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OBJECTIVE: To assess the potential impact of the O-RADS MRI score on the decision-making process for the management of adnexal masses. METHODS: EURAD database (prospective, European observational, multicenter study) was queried to identify asymptomatic women without history of infertility included between March 1st and March 31st 2018, with available surgical pathology or clinical findings at 2-year clinical follow-up. Blinded to final diagnosis, we stratified patients into five categories according to the O-RADS MRI score (absent i.e. non adnexal, benign, probably benign, indeterminate, probably malignant). Prospective management was compared to theoretical management according to the score established as following: those with presumed benign masses (scored O-RADS MRI 2 or 3) (follow-up recommended) and those with presumed malignant masses (scored O-RADS MRI 4 or 5) (surgery recommended). RESULTS: The accuracy of the score for assessing the origin of the mass was of 97.2 % (564/580, CI95% 0.96-0.98) and was of 92.0 % (484/526) for categorizing lesions with a negative predictive value of 98.1 % (415/423, CI95% 0.96-0.99). Theoretical management using the score would have spared surgery in 229 patients (87.1 %, 229/263) with benign lesions and malignancy would have been missed in 6 borderline and 2 invasive cases. In patients with a presumed benign mass using O-RADS MRI score, recommending surgery for lesions >= 100 mm would miss only 4/77 (4.8 %) malignant adnexal tumors instead of 8 (50 % decrease). CONCLUSION: The use of O-RADS MRI scoring system could drastically reduce the number of asymptomatic patients undergoing avoidable surgery.

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BACKGROUND: The need for a concept for the nationwide strategic transfer of critical care patients in Germany was highlighted during the COVID-19 (coronavirus disease 2019) pandemic. Despite the cloverleaf concept developed specifically for this purpose, the transfer of large numbers of critical care patients represents a major challenge. With the help of a computer simulation, the SCATTER research project uses a fictitious example to test, develop, and recommend transfer strategies. METHOD: The simulation was programmed after collecting procedural and structural data on critical care transports within Germany. The simulation allows altering various parameters and testing different transfer scenarios. In a fictitious scenario, nationwide transfers starting from Schleswig-Holstein were simulated and evaluated using predetermined criteria. RESULTS: In the case of ground-based transfers, it became apparent that, depending on the selected target region, not all patients could be transferred due to the limited range of ground-based vehicles. Although a higher number of patients can be transferred by air, this is associated with additional gurney changes and potential risk to the patient. A distance-dependent transport strategy led to the identical results as purely air-bound transport, since air-bound transport was always chosen due to the long distances. DISCUSSION: The simulation can be used to develop recommendations and to draw important conclusions from different transfer strategies.

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OBJECTIVES: To explore experiences of, and perspectives on, health technology assessment (HTA) processes used to produce recommendations about subsidizing new medicines, and medical technologies in Australia, from the perspectives of those experienced in these processes. METHODS: Semi-structured interviews with a diverse group of 18 informants currently or previously members of the Pharmaceutical Benefits Advisory Committee (PBAC) or the Medical Services Advisory Committee (MSAC). Participants were interviewed September 2021-February 2022. Transcripts were analyzed using reflexive thematic analysis. RESULTS: 3 major themes were identified: contrasting technical and decision-making stages, resisting reductionist approaches, and navigating decision-making trade-offs. Participants discussed the complexities of the evaluative HTA process, especially when considering uncertainty in the evidence. As part of the current process, a deliberative decision-making stage was considered essential, allowing a flexible approach to decision making to consider factors beyond strength and quality of quantifiable data in the technical evaluation. Participants acknowledged these less-quantifiable factors were sometimes considered implicitly or were difficult to describe and this, paired with commercial in confidence requirements, presented challenges with respect to the desire to increase transparency. CONCLUSION (S): As HTA processes for new medicines and medical technologies in Australia continue to be reviewed, the balance between retaining flexibility during deliberation, confidentiality for sponsors and the public's desire for greater transparency may be a fruitful area for continuing research.

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