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This study of first year high-school students in South Australia focussed on the effectiveness of using an innovative project-based mathematics strategy to improve the classroom learning environment and the student outcomes of enjoyment, academic efficacy and achievement. As well, we investigated the differential effectiveness of project-based mathematics for male and female students. A sample of 284 students from a co-educational private secondary school provided quantitative data about classroom learning environment as assessed with seven scales from the What Is Happening In this Class? (WIHIC), together with data about the three student outcomes assessed with one scale each. Our study also included a mixed-method design involving qualitative information collected through classroom observations and interviews with 20 students and 3 teachers to explain patterns emerging from the analysis of quantitative data.

Abstract Background The COVID-19 pandemic disrupted routine health care and antenatal and birth services globally. The Shoklo Malaria Research Unit (SMRU) based at the Thailand-Myanmar border provides cross border antenatal care (ANC) and birth services to marginalised pregnant women. The border between the countries entered lockdown in March 2020 preventing cross-border access for women from Myanmar to Thailand. SMRU adapted by opening a new clinic during the COVID-19 pandemic in Myanmar. This study explored the impact of the COVID-19 pandemic and response on access to ANC and pregnancy outcomes for marginalised pregnant women in the border regions between Thailand and Myanmar. Methods A retrospective review of medical records of all pregnancies delivered or followed at antenatal clinics of the SMRU from 2017 to the end of 2022. Logistic regression was done to compare the odds of maternal and neonatal outcomes between women who delivered pre-COVID (2017–2019) and women who delivered in the COVID-19 pandemic (2020–2022), grouped by reported country of residence: Thailand or Myanmar. Results Between 2017 and the end of 2022, there were 13,865 (5,576 resident in Thailand and 8,276 in Myanmar) marginalised pregnant women who followed ANC or gave birth at SMRU clinics. Outcomes of pregnancy were known for 9,748 women with an EGA ≥ 28 weeks. Unknown outcome of pregnancy among women living in Thailand did not increase during the pandemic. However, there was a high (60%) but transient increase in unknown outcome of pregnancy for women with Myanmar residence in March 2020 following border closure and decreasing back to the baseline of 20–30% after establishment of a new clinic. Non-literate women were more likely to have an unknown outcome during the pandemic. There was no statistically significant increase in known stillbirths or maternal deaths during the COVID pandemic in this population but homebirth was over represented in maternal and perinatal mortality. Conclusion Decreasing barriers to healthcare for marginalised pregnant women on the Thailand-Myanmar border by establishment of a new clinic was possible in response to sudden border closure during the COVID-19 pandemic and most likely preventing an increase in maternal and perinatal mortality.

Population aging calls for a thorough understanding of how age impacts subjective wellbeing. Resource theories posit that people need resources, such as health and social relations, to produce subjective wellbeing. Since losing resources is more likely at older age, we examined whether the availability of resources indeed changes with age, and how this affects subjective wellbeing.We analysed data from 75,895 individuals aged 50 and older participating in two or more waves of the Survey of Health, Ageing, and Retirement in Europe. We analysed, separately for women and men, fixed effect regression models, estimating main and interaction effects of age and indicators of health, financial and social resources on two indicators of subjective wellbeing, i.e., life satisfaction and quality of life.We found that the contribution of health to subjective wellbeing does not diminish in later life, and is not different for women and men. Also, having some close others is important for both women and men to maintain subjective wellbeing at every age. However, having a wider social network contributes more to the subjective wellbeing of women than of men at older age. Furthermore, having limited financial means and being widowed might get a bit easier to endure with advanced age, though there is no threshold of age after which any of these resources becomes irrelevant.Health and social policies are needed to strengthen older people's resources, to help maintain subjective wellbeing up to very old age. Gender differences should be taken into account when developing these policies.

Abstract Objective In view of the increasing number of people with (multiple) chronic conditions, the Organisation for Economic Co-operation and Development (OECD) initiated the International Survey of People Living with Chronic Conditions (PaRIS survey), which aims to provide insight in patient-reported outcomes and experiences of chronic care provided by primary care practices to support policy development. The objective of this research note is to describe the structure of the data, collected in the PaRIS survey and how the data will be analysed in a multilevel approach for cross-country comparison. Analysis plan The data structure of the PaRIS survey represents three levels: countries/health systems, primary care practices and patients. Multilevel analysis is used because of its accuracy in estimating country-level outcomes, its flexibility in modelling relationships, and its opportunities in connecting to relevant policy questions. Country-level outcomes will be estimated to facilitate cross-country comparison and (future) within-country comparison over time. Characteristics of patients that potentially explain variation in patient-reported outcomes and experiences can be linked to primary care practice and country/health system characteristics. This makes it possible to address policy-relevant questions relating, e.g., to the impact of chronic care management on patients with a specific chronic condition.

Abstract Background The PaRIS survey, an initiative of the Organisation for Economic Co-operation and Development (OECD), aims to assess health systems performance in delivering primary care by measuring the care experiences and outcomes of people over 45 who used primary care services in the past six months. In addition, linked data from primary care practices are collected to analyse how the organisation of primary care practices and their care processes impact care experiences and outcomes. This article describes the development and validation of the primary care practice questionnaire for the PaRIS survey, the PaRIS-PCPQ. Method The PaRIS-PCPQ was developed based on domains of primary care practice and professional characteristics included in the PaRIS conceptual framework. Questionnaire development was conducted in four phases: (1) a multi-step consensus-based development of the source questionnaire, (2) translation of the English source questionnaire into 17 languages, (3) cross-national cognitive testing with primary care professionals in participating countries, and (4) cross-national field-testing. Results 70 items were selected from 7 existing questionnaires on primary care characteristics, of which 49 were included in a first draft. Feedback from stakeholders resulted in a modified 34-item version (practice profile, care coordination, chronic care management, patient follow-up, and respondent characteristics) designed to be completed online by medical or non-medical staff working in a primary care practice. Cognitive testing led to changes in the source questionnaire as well as to country specific localisations. The resulting 32-item questionnaire was piloted in an online survey and field test. Data from 540 primary care practices from 17 countries were collected and analysed. Final revision resulted in a 34-item questionnaire. Conclusions The cross-national development of a primary care practice questionnaire is challenging due to the differences in care delivery systems. Rigorous translation and cognitive testing as well as stakeholder engagement helped to overcome most challenges. The PaRIS-PCPQ will be used to assess how key characteristics of primary care practices relate to the care experiences and outcomes of people living with chronic conditions. As such, policymakers and care providers will be informed about the performance of primary care from the patient’s perspective.

Summary: Background: Infections and sepsis are leading causes of morbidity and mortality in women during pregnancy and the post-pregnancy period. Using data from the 2017 WHO Global Maternal Sepsis Study, we explored the use of early warning systems (EWS) in women at risk of sepsis-related severe maternal outcomes. Methods: On April 27, 2023, we searched the literature for EWS in clinical use or research in obstetric populations. We calculated the proportion of women for whom each existing EWS identified them as at risk for developing severe maternal outcomes by infection severity (complications and severe maternal outcomes). Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios, and J statistics were calculated to assess EWS performance. Machine learning was used to test the diagnostic potential of routine maternal sepsis markers. Findings: 21 EWS were assessed in 2560 women from 46 countries with suspected or confirmed infections. The NICE Risk Stratification tool, Modified Shock Index, maternity Systemic Inflammatory Response Syndrome, and Early Maternal Infection Prompts scores had high sensitivity (88.1–97.5%) for identifying sepsis-related severe maternal outcomes. The quick Sequential Organ Failure Assessment (SOFA) in Pregnancy score and Obstetrically modified SOFA had high specificity (90.4–100%) for identifying women with sepsis-related severe maternal outcomes. Furthermore, combinations of sepsis markers had very low sensitivity and high specificity using machine learning. Interpretation: No score demonstrated enough diagnostic accuracy to be used alone to identify sepsis. However, obstetric—and sepsis-specific EWS performed better for early identification of maternal sepsis than non-obstetric and non-sepsis-specific scoring systems. There are limitations to applying EWS to real-world data, mainly due to the incompleteness of medical data that hinders EWS effectiveness. There is a need to continue developing and testing criteria for early identification of maternal sepsis. Funding: UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), WHO, Merck for Mothers, U.S. Agency for International Development, Wellcome Trust, and National Institute for Health and Care Research.

Background and purpose: Brachytherapy is treatment of choice for early stage nasal vestibule cancer. Over the years improvements were achieved by means of image guided target definition, interstitial implant techniques and also individual mold techniques. The aim of this study was to improve the technique of the implant so that the need for interstitial catheters can be limited by making use of patient individualized 3D-printed applicators. Materials and Methods: In 19 patients 3D-printed applicators were used to deliver pulse dose rate (PDR) brachytherapy. All patients underwent computed tomography (CT) and magnetic resonance imaging (MRI). A pre-plan with tumor delineation and manually optimized catheter positions to achieve tumor coverage was made. Based on the pre-plan a 3D-printed applicator was manufactured. Dose was evaluated by several indices: Conformity Index, Healthy Tissues Conformity Index, Dose Homogeneity Index, Dose non-uniformity ratio, Conformal index and high dose (HD) index. Results: A high target coverage was achieved, with a median V100%CTV of 99.1 % (range, 81.8–100 %) and median CI of 0.99 (range, 0.82–1.00), as well as a median V0.7GyGTV of 100 % (range, 93.0–100 %). The median HD was 0.39 (range, 0.20–0.83). Interstitial catheters were needed in 12 patients. None of the patients developed grade ≥ II toxicity within the median follow up of 18 months. Conclusions: This study shows that using 3D-printed applicators limits the need for interstitial catheters and also limits the high doses in normal tissue.

Abstract Background Breast density is an important risk factor for breast cancer complemented by a higher risk of cancers being missed during screening of dense breasts due to reduced sensitivity of mammography. Automated, deep learning-based prediction of breast density could provide subject-specific risk assessment and flag difficult cases during screening. However, there is a lack of evidence for generalisability across imaging techniques and, importantly, across race. Methods This study used a large, racially diverse dataset with 69,697 mammographic studies comprising 451,642 individual images from 23,057 female participants. A deep learning model was developed for four-class BI-RADS density prediction. A comprehensive performance evaluation assessed the generalisability across two imaging techniques, full-field digital mammography (FFDM) and two-dimensional synthetic (2DS) mammography. A detailed subgroup performance and bias analysis assessed the generalisability across participants’ race. Results Here we show that a model trained on FFDM-only achieves a 4-class BI-RADS classification accuracy of 80.5% (79.7–81.4) on FFDM and 79.4% (78.5–80.2) on unseen 2DS data. When trained on both FFDM and 2DS images, the performance increases to 82.3% (81.4–83.0) and 82.3% (81.3–83.1). Racial subgroup analysis shows unbiased performance across Black, White, and Asian participants, despite a separate analysis confirming that race can be predicted from the images with a high accuracy of 86.7% (86.0–87.4). Conclusions Deep learning-based breast density prediction generalises across imaging techniques and race. No substantial disparities are found for any subgroup, including races that were never seen during model development, suggesting that density predictions are unbiased.

Abstract Background We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). Methods In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. Results In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95–0.96) and 0.80 (IQR 0.67–0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29–0.76) for tumor segmentation. Conclusions Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. Relevance statement Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist’s workload and increasing accuracy and consistency. Key points • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations. Graphical Abstract

AbstractPatients with Inflammatory Bowel Disease (IBD) are at increased risk of developing malignancies. IBD patients with oral cavity cancer may have reduced survival compared with the general population. This article describes two IBD patients, non-smokers, on long-term use of mesalazine with the development of oral cavity cancer. In IBD the clinician should be aware of possible head and neck malignancies and in case of doubt a biopsy should be performed, even in the absence of standard risk factors.

Abstract Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution. We illustrate the effectiveness of the proposed method to detect and correct performance drift in mammography-based breast cancer screening and on publicly available histopathology data. We show that the proposed method can preserve the expected performance in terms of sensitivity/specificity under various realistic scenarios of image acquisition shift, thus offering an important safeguard for clinical deployment.