Standard Questions Research Articles

Laparoscopic ileopexy for afferent loop syndrome after restorative proctocolectomy—a retrospective case series

BackgroundTo study the effect of laparoscopic ileopexy in patients with afferent-loop syndrome (ALS) after restorative proctocolectomy (RP).MethodIleopexy has been the treatment of choice in patients with ALS for the last 5 years at our department. All patients who had undergone ileopexy for ALS between January 2019 and August 2023 were identified. Data were extracted from the medical records. All patients were contacted and asked standardized questions regarding symptoms of ALS. A symptom score was calculated and compared before surgery and at the last follow-up.ResultsTen patients, who had undergone ileopexy for ALS, were identified. Eight of these (80%) had been admitted with small bowel obstruction due to ALS. The remaining 2 patients had other symptoms indicative of ALS. In all patients, ileopexy was immediately effective in reducing symptoms. Symptoms recurred after 16.5 weeks (2–80) in 8 patients. Repeat laparoscopy showed that the ileopexy had slipped in 6 of these. Six had a new ileopexy with mesh. Later, one of these developed recurrent symptoms and had a new mesh ileopexy performed. No mesh complications were seen. Symptom score was reduced from 6.5 (1–9) to 2 (0–7) (p = 0.02) at the last follow-up.ConclusionsIn this study, ileopexy is effective in reducing symptoms of ALS after RP. In a high proportion of patients, it is necessary to use mesh to ensure long-term fixation of the ileum.

The Clinical Judgment Debriefing Structure: Integrating the Clinical Judgement Measurement Model into debriefing

The transformation of nursing education in the United States, driven by the Next Generation NCLEX (NGN), demands a focus on clinical judgment. However, a gap exists in the literature regarding debriefing structures that center on clinical judgment and integrate the Clinical Judgment Measurement Model (CJMM). This paper introduces the Clinical Judgment Debriefing Structure (CJDS) that was designed to incorporate the CJMM into debriefing to support the development of clinical judgment and enhance NGN preparedness. The CJDS provides clinical judgment practice using reflective questions and tasks by emphasizing each element in Layer 3 of the CJMM. Implementation involves faculty inputting simulation content into the CJDS by preparing answers to the standardized reflection questions and tasks. The CJDS streamlines faculty preparation and ensures standardization of each debriefing session. Presented at the 2023 International Nursing Association for Clinical Simulation and Learning (INACSL) conference, the CJDS received positive feedback, indicating its practicality and applicability in simulation practices. Findings from initial testing of the CJDS using a qualitative/quantitative design, demonstrated increased knowledge and confidence in clinical judgment. While measuring clinical judgment remains challenging, the CJDS stands as a unique tool for centering debriefing on clinical judgment and guiding students through Layer 3 of the CJMM, ultimately contributing to safer nursing practice.

Quitting with pride: Differences in smoking behaviour across sexual identity groups in Ireland

Abstract Background As smoking prevalence reduces, smoking inequalities across population groups is emerging as a key challenge. Despite recognition of specific needs among members of the Lesbian, Gay, Bisexual, Transexual Plus (LGBT+) community, differences in smoking across sexual identity groups is uncharted in Ireland and the community is not represented in tobacco policy agenda setting. This study aimed to measure and compare the prevalence of smoking, quitting intention and quitting behaviour across sexual identify groups in Ireland. Methods Secondary analysis of the 2023 “Healthy Ireland” cross sectional survey of health status was conducted (n = 7,411). For the first time, this survey included a question on self-reported sexual identify, along with standard questions on smoking prevalence, quitting intention and quitting behaviour. Prevalence of smoking, quitting intention and quitting behaviour were calculated with 95% Confidence Intervals (CI) and compared across groups with Chi Square tests. Results In total, 2.3% (95% CI 2.0-2.7%) of respondents reported an LGB+ identity. Compared to those reporting straight identity, LGB+ respondents reported higher smoking prevalence (17.6% versus 21.4% respectively); intention to quit was similar across groups, however, LBG+ respondents also reported a higher prevalence of making at least one quit attempt in the previous 12 months (51.3% versus 78.0% respectively, p < 0.0001). Conclusions While this study highlighted tobacco-use as a health challenge for the LGB+ community in Ireland, it also identified an opportunity for tailored stop smoking approaches to support quit success. As a consequence, an innovative Fulbright-sponsored policy dialogue was convened between leaders in tobacco control, LGBT+ community leaders, health services and academia across Ireland and the USA to explore solutions, underlining the important role equity stratifiers and population surveillance play in agenda setting for public health. Key messages • Including sexual identity in the national population health survey enabled tobacco-use needs across sexual identity groups in Ireland delineated for the first time. • Equity-stratified population health surveillance informed a policy dialogue to better represent needs of LGBT+ community in public health agenda setting.

Systematic Analysis of Retrieval-Augmented Generation-Based LLMs for Medical Chatbot Applications

Artificial Intelligence (AI) has the potential to revolutionise the medical and healthcare sectors. AI and related technologies could significantly address some supply-and-demand challenges in the healthcare system, such as medical AI assistants, chatbots and robots. This paper focuses on tailoring LLMs to medical data utilising a Retrieval-Augmented Generation (RAG) database to evaluate their performance in a computationally resource-constrained environment. Existing studies primarily focus on fine-tuning LLMs on medical data, but this paper combines RAG and fine-tuned models and compares them against base models using RAG or only fine-tuning. Open-source LLMs (Flan-T5-Large, LLaMA-2-7B, and Mistral-7B) are fine-tuned using the medical datasets Meadow-MedQA and MedMCQA. Experiments are reported for response generation and multiple-choice question answering. The latter uses two distinct methodologies: Type A, as standard question answering via direct choice selection; and Type B, as language generation and probability confidence score generation of choices available. Results in the medical domain revealed that Fine-tuning and RAG are crucial for improved performance, and that methodology Type A outperforms Type B.

Implementation and reception of a semi-structured resident interview to improve equity

Abstract Introduction/Objective To improve equity and inclusion in the residency candidacy interview process, we collaborated with our human resources and graduate medical education colleges to develop standardized interview questions based on best practices reported in corporate and medical literature. These standardized questions were piloted by resident interviewers during the 2023-2024 Pathology residency application cycle in a semi-structured interview format. Methods/Case Report With consultants from the graduate medical education and human resources, we identified favored qualities of expectational candidates (e.g., passion for pathology, interest in program, collaboration, reliability, and compassion) and developed a list of standardized questions with a corresponding answer scoring guide to guide objective assessment of answers for each quality. Residents and fellows (“interviewers”) who volunteer to interview Pathology residency candidates (“candidates”) received training on interviewing best practices, identifying and mitigating bias, and the new semi-structured interview format at the beginning of the interview season. Interviewers were encouraged to use a semi-structured interview format where at least one standardized question is included in addition to their usual ad hoc interview questions and discussions. After the interview season concluded, we surveyed all interviewers (n=39) and candidates (n=90) who participated in this process, for feedback on the semi-structured interview process. Results (if a Case Study enter NA) 31/90 (34%) applicants provided feedback about their interview with 15/29 (52%) applicants agreeing that the semi-structured interview format enhanced their experience. 20/31 (65%) applicants agreed that their impression of Stanford would be positively affected if all interviews were conducted in a semi- structured format. 24/31 (77%) think faculty members should use standardized questions while 17/31 (55%) think residents should use standardized questions in either a semi-structured or structured formats. 17/31 (55%) feel that structured interviews contribute to improving equity. 12/39 (31%) of current residents provided feedback about their interviews. All 12 (100%) respondents used standardized questions in a semi-structured interview format. 10/12 (83%) interviewers think the new format improves our resident interview process. Conclusion Overall, responses from both resident interviewers and applicants support the implementation of semi- structured interviews and the continuation and expansion of this initiative.

핵심역량 함양을 위한 초⋅중등학교 교육과정기준 개발 연구: 핵심프로젝트 중심으로

Objectives The purpose of this study is to develop core-project curriculum standards for elementary and middle school schools by assuming the elementary and middle school curriculum for cultivating core competency as a ‘core project’ and exploring the curriculum standard development model for this purpose. Methods To this end, according to the ABCD (A Bridge Curriculum Development) procedure, the basic questions of the core-project curriculum standards for elementary and middle school schools were set and a draft plan was designed, and a draft and revised plan were prepared by reflecting the opinions of elementary and middle school teachers and experts. The final draft was finalized through an expert council and applied to the general outline of the 2015 revised curriculum. Results Firstly, in order to discover core-projects that fit the characteristics of the subject, the ‘10 characteristics’ of core projects that represent the characteristics of core-projects were derived. Secondly, the research and development of the core-project curriculum standards for elementary and middle school schools was materialized through the process of draft, draft, revision, final draft, and application plan. Thirdly, it was confirmed that the elementary and middle school curriculum standards for cultivating core competencies are universally applicable to various subjects such as technology and science. Conclusions Firstly, core-projects can become an important curriculum that allows students to comprehensively learn subject matter and core competencies. Secondly, It was confirmed that there was a need to maximize educational effectiveness by clearly establishing the function, form, and contents of elementary and middle school curriculum standards for cultivating core competency. Thirdly, it was emphasized that the development of curriculum standards should reflect the diverse needs of society, learners, and academics.

A robust visual question answering approach to reduce multimodal bias

Currently, many visual question answering models have bias problems. Specifically, when the question-answer relationship in the training data shows a more obvious mapping relationship, the model shows poor generalization ability. For such biased predictions, existing research work mainly considers language bias, while ignoring the bias information introduced by images. In order to enhance the robustness of visual question answering models, a bias reduction method is proposed, and on this basis, the influence of language and visual information on bias is explored. Furthermore, two bias learning branches are constructed to capture language bias and the bias caused by language and images respectively, and the bias reduction method is used to obtain more robust prediction results. Finally, according to the difference in prediction probability between the standard visual question answering and bias branches, the samples are dynamically weighted, so that the model can dynamically adjust the learning degree for samples with different bias levels. Experiments on datasets such as VQA-CP v2.0 prove the effectiveness of the proposed method and alleviate the influence of bias on the model.

Smooth connectivity in real algebraic varieties

AbstractA standard question in real algebraic geometry is to compute the number of connected components of a real algebraic variety in affine space. This manuscript provides algorithms for computing the number of connected components, the Euler characteristic, and deciding the connectivity between two points for a smooth manifold arising as the complement of a real hypersurface of a real algebraic variety. When considering the complement of the set of singular points of a real algebraic variety, this yields an approach for determining smooth connectivity in a real algebraic variety. The method is based upon gradient ascent/descent paths on the real algebraic variety inspired by a method proposed by Hong, Rohal, Safey El Din, and Schost for complements of real hypersurfaces. Several examples are included to demonstrate the approach.

APPLICATIONS OF MULTIMODAL GENERATIVE ARTIFICIAL INTELLIGENCE IN A REAL-WORLD RETINA CLINIC SETTING.

This study evaluates a large language model, Generative Pre-trained Transformer 4 with vision, for diagnosing vitreoretinal diseases in real-world ophthalmology settings. A retrospective cross-sectional study at Bascom Palmer Eye Clinic, analyzing patient data from January 2010 to March 2023, assesses Generative Pre-trained Transformer 4 with vision's performance on retinal image analysis and International Classification of Diseases 10th revision coding across 2 patient groups: simpler cases (Group A) and complex cases (Group B) requiring more in-depth analysis. Diagnostic accuracy was assessed through open-ended questions and multiple-choice questions independently verified by three retina specialists. In 256 eyes from 143 patients, Generative Pre-trained Transformer 4-V demonstrated a 13.7% accuracy for open-ended questions and 31.3% for multiple-choice questions, with International Classification of Diseases 10th revision code accuracies at 5.5% and 31.3%, respectively. Accurately diagnosed posterior vitreous detachment, nonexudative age-related macular degeneration, and retinal detachment. International Classification of Diseases 10th revision coding was most accurate for nonexudative age-related macular degeneration, central retinal vein occlusion, and macular hole in OEQs, and for posterior vitreous detachment, nonexudative age-related macular degeneration, and retinal detachment in multiple-choice questions. No significant difference in diagnostic or coding accuracy was found in Groups A and B. Generative Pre-trained Transformer 4 with vision has potential in clinical care and record keeping, particularly with standardized questions. Its effectiveness in open-ended scenarios is limited, indicating a significant limitation in providing complex medical advice.

Large Language Models and the North American Pharmacist Licensure Examination (NAPLEX) Practice Questions

ObjectiveThis study aims to test the accuracy of large language models (LLMs) in answering standardized pharmacy examination practice questions. MethodsThe performance of 3 LLMs (generative pretrained transformer [GPT]−3.5, GPT-4, and Chatsonic) was evaluated on 2 independent North American Pharmacist Licensure Examination practice question sets sourced from McGraw Hill and RxPrep. These question sets were further classified into binary question categories of adverse drug reaction (ADR) questions, scenario questions, treatment questions, and select-all questions. Python was used to run χ2 tests to compare model and question-type accuracy. ResultsOf the 3 LLMs tested, GPT-4 achieved the highest accuracy, with 87% accuracy on the McGraw Hill question set and 83.5% accuracy on the RxPrep question set. In comparison, GPT-3.5 had 68.0% and 60.0% accuracy on those question sets, respectively, and Chatsonic had 60.5% and 62.5% accuracy on those question sets, respectively. All models performed worse on select-all questions compared with non-select-all questions (GPT-3: 42.3% vs 66.2%; GPT-4: 73.1 vs 87.2%; Chatsonic: 36.5% vs 71.6%). GPT-4 had statistically higher accuracy in answering ADR questions (96.1%) compared with non-ADR questions (83.9%). ConclusionOur study found that GPT-4 outperformed GPT-3.5 and Chatsonic in answering North American Pharmacist Licensure Examination pharmacy licensure examination practice questions, particularly excelling in answering questions related to ADRs. These results suggest that advanced LLMs such as GPT-4 could be used for applications in pharmacy education.

Family history and genetic risk score combined to guide cancer risk stratification: A prospective cohort study.

Family history (FH) of cancer and polygenic risk scores (PRS) are pivotal for cancer risk assessment, yet their combined impact remains unclear. Participants in the UK Biobank (UKB) were recruited between 2006 and 2010, with complete follow-up data updated until February 2020 for Scotland and January 2021 for England and Wales. Using UKB data (N = 442,399), we constructed PRS and incidence-weighted overall cancer PRS (CPRS). FH was assessed through self-reported standardized questions. Among 202,801 men (34.6% with FH) and 239,598 women (42.0% with FH), Cox regression was used to examine the associations between FH, PRS, and cancer risk. We found a significant dose-response relationship between FH of cancer and corresponding cancer risk (Ptrend < .05), with over 10 significant pairs of cross-cancer effects of FH. FH and PRS are positively correlated and independent. Joint effects of FH of cancer (multiple cancers) and PRS (CPRS) on corresponding cancer risk were observed: for instance, compared with participants with no FH of cancer and low PRS, men with FH of cancer and high PRS had the highest risk of colorectal cancer (hazard ratio [HR]: 3.69, 95% confidence interval [CI]: 3.01-4.52). Additive interactions were observed in prostate and overall cancer risk for men and breast cancer for women, with the most significant result being a relative excess risk of interaction (RERI) of 2.98, accounting for ~34% of the prostate cancer risk. In conclusion, FH and PRS collectively contribute to cancer risk, supporting their combined application in personalized risk assessment and early intervention strategies.

Health information sharing – conceptual clarification and exploratory analysis in the context of mammography screening

Introduction. Information sharing is an important but under researched type of health information behaviour. In this study, we explore the characteristics of sharing behaviours, their reasons and determinants in the context of mammography screening. Method. We conducted a two-wave online survey with N = 1,136 women. In the first wave, women saw an invitation letter to mammography screening and behavioural determinants were captured with established scales. In the second wave, women reported on their information behaviour between the two waves, combining standardized and open-ended questions. Analysis. We performed qualitative content analysis and a stepwise logistic regression analysis. Results. About one-fifth of the sample shared information on mammography between the two survey waves. Qualitative results revealed different causes and purposes of information sharing. Quantitative results showed that issue importance, opinion leadership and different types of information retrieval were related to information sharing. Conclusions. The results shed light on two facets for the conceptual clarification of health information sharing: sharing in the sense of selecting and passing on information (cf. diffusion of information by opinion leaders) and sharing as the mutual exchange of opinions and experiences. Both facets should be considered to comprehensively examine health information sharing behaviours and their relation with informed decision-making.

Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model’s language capability. Additionally, GPT-4o’s vision and speech capabilities are tested through image classification and object-recognition tasks, as well as accent classification. The multimodal evaluation assesses the model’s performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning capabilities, excelling in tasks that require few-shot learning. GPT-4o also provides notable improvements in multimodal tasks compared to its predecessors. However, the model shows variability and faces limitations in handling complex and ambiguous inputs, particularly in audio and vision capabilities. This paper highlights the need for more comprehensive benchmarks and robust evaluation frameworks, encompassing qualitative assessments involving human judgment, as well as error analysis. Future work should focus on expanding datasets, investigating prompt-based assessment, and enhancing few-shot learning techniques to test the model’s practical applicability and performance in real-world scenarios.

Virtual Reality as an Adjunct to Traditional Patient Counseling in Patients With Newly Diagnosed Localized Prostate Cancer

ObjectivesTo determine the utility of a virtual reality (VR) model constructed using patient-derived clinical imaging to improve patient understanding of localized prostate cancer (PCa) diagnosis and surgical plan. MethodsPatients undergoing robotic radical prostatectomy were selected and demographic data recorded. Patients completed a questionnaire to assess baseline knowledge of their diagnosis after consultation and shared-decision making with their surgeon. A trained non-clinical staff member then guided the patient through a VR experience to view patient-specific anatomy in a 3-dimensional space. Patients then completed the same questionnaire, followed by an additional post-VR questionnaire evaluating patient satisfaction. Questions 1-7 (patient understanding of prostate cancer and treatment plan) and 11-17 (patient opinion of VR) used a standard Likert scale and Questions 8-10 were multiple choice with 1 correct answer. ResultsIn total, 15 patients were included with an average age of 64.1 years. 6 of 7 questions showed an improvement after VR (p<0.001). The percentage of correct responses on Questions 8-10 were higher after VR but not statistically significant (p>0.13). Mean responses range from 4.3 to 4.8 (Likert scale, 1 through 5) for the post-VR questionnaire, with a mean total of 31.9 out of 35. ConclusionThis small preliminary investigation of a novel technology to improve the patient experience showed potential as an adjunct to traditional patient counseling. However, due the small sample size and study design, further research is needed to determine the value VR adds to prostate cancer surgical counselling.

“Let the Driver off the Hook?” moral decisions of autonomous cars and their impact on consumer well-being

Equipped with sophisticated, AI-based driver assistance systems, passenger cars are becoming increasingly intelligent. It seems that in a matter of a few years, fully autonomous vehicles will operate without any driver intervention. In this context, researchers are addressing the question of how fully automated vehicles should make decisions in critical situations. Should they spare the driver, children jumping out into the road or elderly people standing on the sidewalk? Projects such as MIT’s Moral Machine are investigating the preferences of people from different nations and cultures for ethical decision algorithms. Evaluations of these automated decisions and how the may impact consumer perception and well-being are still scarce. In our experimental study, participants experienced a simulator-based driving situation in a fully autonomous car, after which they were confronted with alternative scenarios requiring automated action by the car in a critical situation. We measured the emotional status and well-being of our test-persons (N=33) in those critical situations using facial expression recognition (FER), electroencephalography (EEG), and standardized questions. The results show that there are detectable differences between the scenarios with respect to emotions as well as subjective well-being and behavioral intentions in the test group’s responses to the questionnaire. Regarding FER and EEG, no statistically significant differences could be shown due to the small subsample.

Using systems modeling to facilitate undergraduate physiology student learning and retention of difficult concepts.

Physiology concepts, such as acid-base balance, may be difficult for students to understand. Systems modeling, a cognitive tool, allows students to visualize their mental model of the problem space to enhance learning and retention. Methods: We performed a within-subjects three-period randomized control comparison of systems modeling versus written discussion activities in an undergraduate asynchronous online Anatomy and Physiology II course. Participants (n=108) were randomized to groups with differing treatment orders across three course units: endocrine, immune, and acid-base balance. Participants demonstrated content understanding either through constructing systems modeling diagrams (M) or written discussion posts (W) in a MWM, MMW, or WMM sequence. Results: For each of three units, student performance was assessed on six standardized multiple-choice questions embedded within a 45-question exam. The same six questions per unit, eighteen questions total, was again assessed on the 75-question final exam. The groups demonstrated no significant difference in performance in the endocrine unit exam (mean difference, MD=-0.036). However, the modeling group outperformed the writing group in the immune unit exam (MD=0.209) and widened the gap in the acid-base balance unit exam (MD=0.243). On the final exam, performance was again higher for the modeling group on acid-base balance content as mean difference increased to 0.306 despite the final exam content for acid-base balance being significantly more difficult compared to other units (modeling: F(2)=29.882, p<.001; writing: F(2)=25.450, p<.001). Conclusions: These results provide initial evidence that participation in systems modeling activities enhance student learning of difficult physiology content as evidenced by improved multiple-choice question performance.

Open-Source Data Formalization through Model-Based Systems Engineering for Concurrent Preliminary Design of CubeSats

Market trends in the space sector suggest a notable increase in satellite operations and market value for the coming decade. In parallel, there has been a shift in the industrial and academic sectors from traditional Document-Based System Engineering to Model-based systems engineering (MBSE) combined with Concurrent engineering (CE) practices. Due to growing demands, the drivers behind this change have been the need for quicker and more cost-effective design processes. A key challenge in this transition remains to determine how to effectively formalize and exchange data during all design stages and across all discipline-specific tools; as representing systems through models can be a complex endeavor. For instance, during the Preliminary design (PD) phase, the integration of system models with external mathematical models for simulations, analyses, and system budgeting is crucial. The introduction of CubeSats and their standard has partly addressed the question of standardization and has aided in reducing overall development time and costs in the space sector. Nevertheless, questions about how to successfully exchange data endure. This paper focuses on formalizing a CubeSat model for use across various stages of the PD phase. The entire process is conducted with the exclusive use of open-source tools, to facilitate the transparency of data integration across the PD phases, and the overall life cycle of a CubeSat. The paper has two primary outcomes: (i) developing a generic CubeSat model using Systems modeling language (SysML) that includes data storage and visualization through the application of Unified modeling language (UML) stereotypes, streamlining in parallel information exchange for integration with various simulation and analysis tools; (ii) creating an end-to-end use case scenario within the Nanostar software suite (NSS), an open-source framework designed to streamline data exchange across different software during CE sessions. A case study from a theoretical academic space mission concept is presented as the illustration of how to utilize the proposed formalization, and it serves as well as a preliminary validation of the proposed formalization. The proposed formalization positions the CubeSat SysML model as the central data source throughout the design process. It also supports automated trade-off analyses by combining the benefits of SysML with effective data instantiating across all PD study phases.

Comparison of medication abortion outcomes at less than and greater than 6 weeks gestation

ObjectivesTo compare outcomes and characteristics of two cohorts of patients: those receiving medication abortion (MAB) at ≤42 days gestation and those at 43-56 days gestation. Study DesignWe conducted a retrospective cohort study to compare characteristics and outcomes of all 142 patients accessing MAB at ≤42 days versus 200 patients at 43-56 days in 2022. We sought to detect a 7% difference in MAB success with 80% power and an alpha of 0.05. We compared follow-up responses and unscheduled contacts with the healthcare system. ResultsAbortion success rates were similar between the ≤42 day and 43-56 day groups (94.3% vs 97%, p=0.226). Those ≤42 days had a higher frequency of unscheduled office visits (13% vs 6%, p=0.01) but no difference in phone calls or emergency room visits. More patients with successful MAB in the ≤42 days group answered that bleeding (11.7% vs 1.9%, p=0.006) and cramping (10.5% vs 2.9%, p=0.035) were not heavier or worse than a period, and that they did not have pregnancy symptoms prior to the abortion (15.8% vs 6.0%, p=0.034).Patients ≤42 days gestation less often had a pre-treatment ultrasound (48% vs 64%, p=0.004). Patients without prior ultrasound more often needed uterine aspiration to complete the abortion (6.8% vs 2%, p=0.027). ConclusionsPatients undergoing MAB at ≤42 days have similar success rates but more unscheduled office visits, and more ambiguous symptoms when using standardized questions for evaluating abortion success. Clinicians should consider adapting their anticipatory guidance and counseling for this population. ImplicationsAccess to very early abortion is increasingly relevant as legal restrictions on abortion increase. Earlier gestations may have different responses to standard follow-up questions despite a successful MAB and may have more interactions with the healthcare system.

Remitted euros are not equal: The complex spending behaviour of Kosovar households

AbstractThis article uses a mixed‐methods approach to evaluate how remittances impact household spending behaviour in Kosovo. Based on a nationally representative data set and using matching techniques, it shows that migration does not cause substantive changes in household spending. While remittances cover basic consumption needs, there are no impacts on budgetary allocations for business investments, health and education. Yet, qualitative research conducted in Kosovo's Opoja region, involving the analysis of 28 open‐ended interviews, unveils a complex remitting mechanism. While migrants send smaller but regular amounts of money to cover basic needs, earmarked transfers are allocated for housing investments and conspicuous consumption only. These earmarked transfers do not enter Kosovar households' budgets, as migrants exercise decision‐making authority over these funds. For policymakers, our findings suggest that standard survey questions fall short of capturing the complexity of the remitting mechanism. When promoting productive investments, it is the migrants who should be targeted as key decision‐makers.

Validity of the Systemizer Profile Questionnaire: A New Tool to Identify Cognitive, Mentalizing, Sensory, Social, and Systemizing Abilities in Adults with Autism-Spectrum-Disorders With and Without Comorbid ADHD.

Systemizer Profile Questionnaire (SPQ), which has not been used before, investigates difficulties in mentalisation, sensory- and/or social sensitivity and social cognition (MSSSC) in subjects with Autism-Spectrum-Disorders (ASD) with and without Attention-Deficit-Hyperactivity-Disorder (ADHD). The aim of this study was to evaluate the reliability and validity of the SPQ domains, and to assess the predictive validity of the SPQ against the Ritvo Autism Asperger Diagnostic Scale (RAADS). Three-hundred-fifty-four study subjects with ICD-10 verified ASD confirmed by RAADS and 354 controls matched on age group and gender were recruited and evaluated systematically with SPQ, standardized questions about demographic and clinical data. Hypothesized SPQ subscales formed from 85 items were evaluated using confirmatory factor analysis (CFA). Resulting revised sub-scales were confirmed using item response theory (IRT) and the predictive validity of the SPQ scores was evaluated using RAADS scores above 64 as the standard. Twenty-two of the original 85 items were removed, resulting in an instrument with 63 items across nine psychometrically valid domains. These domains had high sensitivity (range: 0.64 to 0.84), and high specificity (range: 0.73 to 0.90). Positive predictive values (range: 0.76 to 0.89) and negative predictive values (range: 0.69 to 0.90) were also high. For the total SPQ score the sensitivity was 0.95, the specificity was 0.87, the positive predictive value was 0.88 and the negative predictive value was 0.95. SPQ domains are valid descriptions/profiles of MSSSC given that ASD is confirmed by RAADS, though irrelevant if not, as SPQ is not a diagnostic instrument.