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The renewed interest in the relationship between the Gospel of Mark and the apostle Paul has led to more nuanced arguments for Paul's influence on Mark based on refined methodologies. In this article, I critically evaluate the current state of the argument, concluding that it depends on too many variables and expects too much of our sparse evidence to make a convincing argument that Mark is distinctly Pauline. Therefore, the Gospel of Mark should be read independently of Paul so that its unique features might be first understood within its own narrative world, rather than interpreted through a Pauline lens., The relationship between the Gospel of Mark and the apostle Paul has received enormous scholarly attention over the past few decades. For much of the twentieth century, Martin Werner's 1923 [...]

Background: Individual risk prediction of 5-year locoregional recurrence (LRR) and contralateral breast cancer (CBC) supports decisions regarding personalised surveillance. The previously developed INFLUENCE tool was rebuild, including a recent population and patients who received neoadjuvant systemic therapy (NST). Methods: Women, surgically treated for nonmetastatic breast cancer, diagnosed between 2012 and 2016, were selected from the Netherlands Cancer Registry. Cox regression with restricted cubic splines was compared to Random Survival Forest (RSF) to predict five-year LRR and CBC risks. Separate models were developed for NST patients. Discrimination and calibration were assessed by 100x bootstrap resampling. Results: In the non-NST and NST group, 49,631 and 10,154 patients were included, respectively. Age, mode of detection, histology, sublocalisation, grade, pT, pN, hormonal receptor status ± endocrine treatment, HER2 status ± targeted treatment, surgery ± immediate reconstruction ± radiation therapy, and chemotherapy were significant predictors for LRR and/or CBC in non-NST patients. For NST patients this was similar, but excluding (y)pT and (y)pN status, and including presence of ductal carcinoma in situ, axillary lymph node dissection and pathologic complete response.For non-NST patients, the Cox and RSF models were integrated in the online tool with 5-year AUCs of 0.77 (95%CI:0.77–0.77) and 0.68 (95%CI:0.67–0.68)] for LRR and CBC prediction, respectively. For NST patients, the RSF model performed best (AUCs 0.77 (95%CI:0.76–0.78) and 0.73 (95%CI:0.69–0.76) for LRR and CBC, respectively). Regarding calibration, observed-predicted differences were all

Background and purpose: The COVID-19 pandemic resulted in an accelerated recommendation to use five-fraction radiotherapy schedules, according to the FAST- and FAST-Forward trial. In this study, trends in the use of different radiotherapy schedules in the Netherlands were studied, as well as the likelihood of receiving five fractions. Materials and methods: Data from the NABON Breast Cancer Audit-Radiotherapy and Netherlands Cancer Registry was used. Women receiving radiotherapy for their primary invasive breast cancer or DCIS between 01–01-2020 and 31–12-2021 were included. Logistic regression was used to investigate the association between patient-, tumour-, treatment-, and radiotherapy institution-related characteristics and the likelihood of receiving five fractions in tumours meeting the FAST and FAST-Forward criteria. Results: Detailed information about radiotherapy treatment was available for 9,392 tumours. Shortly after the start of the COVID-19 pandemic, i.e. April 2020, 19% of the tumours being treated with radiotherapy received five fractions of 5.2 or 5.7 Gray (Gy). While only 3% of the tumours received five fractions in March 2020. The usage of five fractions increased to 26% in December 2021. Partial breast irradiation, compared to whole breast irradiation, was significantly associated with the administration of five fractions, as well as radiotherapy delivered in an academic radiotherapy institution compared to an independent institution. Conclusion: The start of the COVID-19 pandemic was associated with the early use of ultra-hypofractionated radiotherapy schedules. After publication of the trials, and mainly after the recommendation by the national radiotherapy society, the implementation further increased. These schedules were not yet used in all patients meeting the eligibility criteria for the FAST- or FAST-Forward trial.

Background: This study continues the review by Gøtzsche and Johansen (Cochrane Database of Systematic Reviews, 2008, Art. No: CD001187), aiming to systematically generate hypotheses on the effectiveness of (sub)strategies for house dust mite allergen avoidance in the treatment of allergic asthma. Methods: We used the trials previously analysed by Gøtzsche and Johansen and searched recently published studies. Data on asthma symptom scores (ASS), ACQ, number of improved patients, AQLQ-scores, medication use, FEV1%, PC20, and FeNO levels were analysed. The effectiveness of strategies was assessed using Metafor in R. Results: Thirty-five trials involving 2419 patients were included in the final study. The patient-reported outcome number of patients with improved condition following total bedroom control was RR = 3.39 (95% confidence interval: 1.04 to 11.04, P = 0.04). The mean differences in the ASS by nocturnal air purification was −0.7 (95% confidence interval: −1.08 to −0.32, P

Airports, the essential hubs of global travel, have to cater for the increasing demands for air travel, with growing passenger numbers and the associated growth in resource consumption. While the aviation sector prioritizes reducing environmental impact in the air, substantial waste is generated at airports. This necessitates a critical examination of waste management practices, especially since a Circular Economy (CE) approach is gaining momentum within the aviation sector. This article introduces the Baseline Circular Airports Method (BCAM), a methodology developed and rigorously tested at Schiphol Amsterdam airport. BCAM systematically analyzes resource streams, considering composition and relevant stakeholders, treatment processes, and environmental impact. By doing so, it establishes strategic prioritization of resource streams for airports to perform focused and effective interventions. BCAM analysis reveals that the highest impact of operational resource streams are Residual, Plastic, Swill, Paper, and International Catering Waste (CAT1), and that corresponding waste management efficiencies can be determined. These outcomes serve as a baseline for ongoing monitoring, offering airports a starting point for strategic planning and assessing progress towards sustainable waste management and CE transitions.

Abstract Background Follow-up of curatively treated primary breast cancer patients consists of surveillance and aftercare and is currently mostly the same for all patients. A more personalized approach, based on patients’ individual risk of recurrence and personal needs and preferences, may reduce patient burden and reduce (healthcare) costs. The NABOR study will examine the (cost-)effectiveness of personalized surveillance (PSP) and personalized aftercare plans (PAP) on patient-reported cancer worry, self-rated and overall quality of life and (cost-)effectiveness. Methods A prospective multicenter multiple interrupted time series (MITs) design is being used. In this design, 10 participating hospitals will be observed for a period of eighteen months, while they -stepwise- will transit from care as usual to PSPs and PAPs. The PSP contains decisions on the surveillance trajectory based on individual risks and needs, assessed with the ‘Breast Cancer Surveillance Decision Aid’ including the INFLUENCE prediction tool. The PAP contains decisions on the aftercare trajectory based on individual needs and preferences and available care resources, which decision-making is supported by a patient decision aid. Patients are non-metastasized female primary breast cancer patients (N = 1040) who are curatively treated and start follow-up care. Patient reported outcomes will be measured at five points in time during two years of follow-up care (starting about one year after treatment and every six months thereafter). In addition, data on diagnostics and hospital visits from patients’ Electronical Health Records (EHR) will be gathered. Primary outcomes are patient-reported cancer worry (Cancer Worry Scale) and overall quality of life (as assessed with EQ-VAS score). Secondary outcomes include health care costs and resource use, health-related quality of life (as measured with EQ5D-5L/SF-12/EORTC-QLQ-C30), risk perception, shared decision-making, patient satisfaction, societal participation, and cost-effectiveness. Next, the uptake and appreciation of personalized plans and patients’ experiences of their decision-making process will be evaluated. Discussion This study will contribute to insight in the (cost-)effectiveness of personalized follow-up care and contributes to development of uniform evidence-based guidelines, stimulating sustainable implementation of personalized surveillance and aftercare plans. Trial registration Study sponsor: ZonMw. Retrospectively registered at ClinicalTrials.gov (2023), ID: NCT05975437.

In the last 60 years, the proportion of white Americans expressing anti-black prejudice in face-to-face survey interviews has declined dramatically. To test whether social desirability pressures affect the expression of anti-black prejudice, we analyzed a within-subjects experiment in the 2008 American National Election Study in which white respondents first reported their endorsement of stereotypes of blacks confidentially via audio computer-assisted self-interviewing (ACASI) and weeks or months later orally during second interviews. Shifting to ACASI led to a small but significant increase in negative views of blacks. Unexpectedly, shifting to ACASI also led to a similarly large increase in negative views of whites. Furthermore, the ACASI reports had no more predictive validity than did the oral reports. This evidence suggests that social desirability pressures do not seriously compromise oral reports of racial stereotypes in face-to-face interviews.

Introduction: Numerous prediction models have been developed to support treatment-related decisions for breast cancer patients. External validation, a prerequisite for implementation in clinical practice, has been performed for only a few models. This study aims to externally validate published clinical prediction models using population-based Dutch data. Methods: Patient-, tumor- and treatment-related data were derived from the Netherlands Cancer Registry (NCR). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), scaled Brier score, and model calibration. Net benefit across applicable risk thresholds was evaluated with decision curve analysis. Results: After assessing 922 models, 87 (9%) were included for validation. Models were excluded due to an incomplete model description (n = 262 (28%)), lack of required data (n = 521 (57%)), previously validated or developed with NCR data (n = 45 (5%)), or the associated NCR sample size was insufficient (n = 7 (1%)). The included models predicted survival (33 (38%) overall, 27 (31%) breast cancer-specific, and 3 (3%) other cause-specific), locoregional recurrence (n = 7 (8%)), disease free survival (n = 7 (8%)), metastases (n = 5 (6%)), lymph node involvement (n = 3 (3%)), pathologic complete response (n = 1 (1%)), and surgical margins (n = 1 (1%)). Seven models (8%) showed poor (AUC

Introduction: Autologous transplantation of spermatogonial stem cells (SSCs) isolated from cryopreserved testicular biopsies obtained before oncological treatment could restore fertility in male childhood cancer survivors. There is a clear necessity for in vitro propagation of the limited SSCs from the testicular biopsy prior to transplantation due to limited numbers of spermatogonia in a cryopreserved testicular biopsy. Still, there is no consensus regarding their optimal culture method.Methods: We performed a systematic review and meta-analysis of studies reporting primary testicular cell cultures of human and non-human primate origin through use of Pubmed, EMBASE, and Web of Science core collection databases. Of 760 records, we included 42 articles for qualitative and quantitative analysis. To quantify in vitro spermatogonial propagation, spermatogonial colony doubling time (CDT) was calculated, which measures the increase in the number of spermatogonial colonies over time. A generalized linear mixed model analysis was used to assess the statistical effect of various culture conditions on CDT.Results: Our analysis indicates decreased CDTs, indicating faster spermatogonial propagation in cultures with a low culture temperature (32°C); with use of non-cellular matrices; use of StemPro-34 medium instead of DMEM; use of Knockout Serum Replacement; and when omitting additional growth factors in the culture medium.Discussion: The use of various methods and markers to detect the presence of spermatogonia within the reported cultures could result in detection bias, thereby potentially influencing comparability between studies. However, through use of CDT in the quantitative analysis this bias was reduced. Our results provide insight into critical culture conditions to further optimize human spermatogonial propagation in vitro, and effectively propagate and utilize these cells in a future fertility restoration therapy and restore hope of biological fatherhood for childhood cancer survivors.

This paper summarizes the discourse on student ratings of teaching in higher education. It reconceptualizes student ratings within a larger process of promoting quality in teaching and student learning. As students engage in productive dialogue with teachers and administrators, metrics drawn from decontextualized surveys are admittedly a vital resource. Our paper contends, however, that student ratings can only become a tool for enhancement when they feed reflective conversations about improving the learning process and when these conversations are informed by the scholarship of teaching and learning. We illustrate this view with a case study of an Engineering Faculty that uses three interconnected initiatives to support an evolving conversation on quality among teachers in partnership with students. The role of student engagement in enhancement efforts is discussed as well as potential challenges to implementation. Our purpose is to spark wider discussion of a systems perspective on student ratings that supports a coherent discourse towards positive change.

The impact of the COVID-19 pandemic on curricular aspects of student learning is already widely studied. However, fewer data exist for its impact on the efficacy of remote academic support. This case study summarizes the findings from a remote tutoring experience questionnaire completed during the Fall 2020 semester by tutors and tutees at a private, R2 university in the United States of America. Our findings are cause for optimism. Technology and physical and emotional wellbeing did not present formidable barriers; tutoring continued to promote student social integration; and the quality of remote tutoring was comparable to that of in-person tutoring. Nevertheless, specific areas faced greater barriers, particularly academic support for math-related tutoring and freshman social integration.

There is a growing interest in the organisational resilience. The internal audit function can contribute to growing and maintaining organisational resilience by including the topic in the internal audit plan. Auditing it requires a reference model. The study described used a mixed methods approach to develop a reference model for auditing organisational resilience. Six relevant hard and soft (behavioural) elements of resilience are determined: people, culture, strategy, processes, governance and regulation. The internal audit function can use the tool to assess attention areas to include in the audit scope and formulate a specific reference framework for the organisation.

Abstract Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ c -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ( $$c$$ c -index $$\sim \,0.63$$ ∼ 0.63 ), and in the case of XGB even better ( $$c$$ c -index $$\sim 0.73$$ ∼ 0.73 ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.